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https://proceedings.mlr.press/v235/hao24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hao24b/hao24b.pdf
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https://openreview.net/forum?id=KmCoS6WkgG
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Data-efficient Large Vision Models through Sequential Autoregression
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https://proceedings.mlr.press/v235/hao24b.html
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Zhiwei Hao, Jianyuan Guo, Chengcheng Wang, Yehui Tang, Han Wu, Han Hu, Kai Han, Chang Xu
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https://proceedings.mlr.press/v235/hao24b.html
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ICML 2024
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Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model’s agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM.
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https://proceedings.mlr.press/v235/hao24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hao24c/hao24c.pdf
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https://openreview.net/forum?id=OjBW993g79
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MGit: A Model Versioning and Management System
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https://proceedings.mlr.press/v235/hao24c.html
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Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang
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https://proceedings.mlr.press/v235/hao24c.html
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ICML 2024
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New ML models are often derived from existing ones (e.g., through fine-tuning, quantization or distillation), forming an ecosystem where models are related to each other and can share structure or even parameter values. Managing such a large and evolving ecosystem of model derivatives is challenging. For instance, the overhead of storing all such models is high, and models may inherit bugs from related models, complicating error attribution and debugging. In this paper, we propose a model versioning and management system called MGit that makes it easier to store, test, update, and collaborate on related models. MGit introduces a lineage graph that records the relationships between models, optimizations to efficiently store model parameters, and abstractions over this lineage graph that facilitate model testing, updating and collaboration. We find that MGit works well in practice: MGit is able to reduce model storage footprint by up to 7$\times$. Additionally, in a user study with 20 ML practitioners, users complete a model updating task 3$\times$ faster on average with MGit.
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https://proceedings.mlr.press/v235/hao24d.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hao24d/hao24d.pdf
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https://openreview.net/forum?id=X7UnDevHOM
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DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
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https://proceedings.mlr.press/v235/hao24d.html
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Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
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https://proceedings.mlr.press/v235/hao24d.html
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ICML 2024
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Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings. However, it is largely in its infancy due to the inherent complexity and diversity, such as long trajectories, multiple scales and varying dimensions of partial differential equations (PDEs) data. In this paper, we present a new auto-regressive denoising pre-training strategy, which allows for more stable and efficient pre-training on PDE data and generalizes to various downstream tasks. Moreover, by designing a flexible and scalable model architecture based on Fourier attention, we can easily scale up the model for large-scale pre-training. We train our PDE foundation model with up to 0.5B parameters on 10+ PDE datasets with more than 100k trajectories. Extensive experiments show that we achieve SOTA on these benchmarks and validate the strong generalizability of our model to significantly enhance performance on diverse downstream PDE tasks like 3D data.
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https://proceedings.mlr.press/v235/harker24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/harker24a/harker24a.pdf
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https://openreview.net/forum?id=xwxUbBHC1q
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Convergence Guarantees for the DeepWalk Embedding on Block Models
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https://proceedings.mlr.press/v235/harker24a.html
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Christopher Harker, Aditya Bhaskara
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https://proceedings.mlr.press/v235/harker24a.html
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ICML 2024
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Graph embeddings have emerged as a powerful tool for understanding the structure of graphs. Unlike classical spectral methods, recent methods such as DeepWalk, Node2Vec, etc. are based on solving nonlinear optimization problems on the graph, using local information obtained by performing random walks. These techniques have empirically been shown to produce “better” embeddings than their classical counterparts. However, due to their reliance on solving a nonconvex optimization problem, obtaining theoretical guarantees on the properties of the solution has remained a challenge, even for simple classes of graphs. In this work, we show convergence properties for the DeepWalk algorithm on graphs obtained from the Stochastic Block Model (SBM). Despite being simplistic, the SBM has proved to be a classic model for analyzing the behavior of algorithms on large graphs. Our results mirror the existing ones for spectral embeddings on SBMs, showing that even in the case of one-dimensional embeddings, the output of the DeepWalk algorithm provably recovers the cluster structure with high probability.
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https://proceedings.mlr.press/v235/harviainen24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/harviainen24a/harviainen24a.pdf
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https://openreview.net/forum?id=JVORowD4MD
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Estimating the Permanent by Nesting Importance Sampling
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https://proceedings.mlr.press/v235/harviainen24a.html
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Juha Harviainen, Mikko Koivisto
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https://proceedings.mlr.press/v235/harviainen24a.html
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ICML 2024
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Sequential importance sampling (SIS) is one of the prominent methods for estimating high-dimensional integrals. For example, it is empirically the most efficient method known for estimating the permanent of nonnegative matrices, a notorious problem with numerous applications in computer science, statistics, and other fields. Unfortunately, SIS typically fails to provide accuracy guarantees due to difficulties in bounding the variance of the importance weights; for estimating the permanent with accuracy guarantees, the most efficient practical methods known are based on rejection sampling. Taking the best of both worlds, we give a variant of SIS, in which sampling is proportional to the upper bound used in rejection sampling. We show that this method is provably more efficient than its rejection sampling counterpart, particularly in high accuracy regimes. On estimating the permanent, we empirically obtain up to two orders-of-magnitude speedups over a state-of-the-art rejection sampling method.
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https://proceedings.mlr.press/v235/hashimoto24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hashimoto24a/hashimoto24a.pdf
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https://openreview.net/forum?id=w9nxTXuaCc
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Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction
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https://proceedings.mlr.press/v235/hashimoto24a.html
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Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
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https://proceedings.mlr.press/v235/hashimoto24a.html
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ICML 2024
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Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: $C^*$-algebraic ML $-$ a cross-fertilization between $C^*$-algebra and machine learning. The mathematical concept of $C^*$-algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use $C^*$-algebras in machine learning, and provide technical considerations that go into the design of $C^*$-algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in $C^*$-algebraic ML and give our thoughts for future development and applications.
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https://proceedings.mlr.press/v235/havens24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/havens24a/havens24a.pdf
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https://openreview.net/forum?id=EEinDTdKr1
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Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
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https://proceedings.mlr.press/v235/havens24a.html
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Aaron J Havens, Alexandre Araujo, Huan Zhang, Bin Hu
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https://proceedings.mlr.press/v235/havens24a.html
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ICML 2024
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Self-attention has been widely used in various machine learning models, such as vision transformers. The standard dot-product self-attention is arguably the most popular structure, and there is a growing interest in understanding the mathematical properties of such attention mechanisms. This paper presents a fine-grained local sensitivity analysis of the standard dot-product self-attention, leading to new non-vacuous certified robustness results for vision transformers. Despite the well-known fact that dot-product self-attention is not (globally) Lipschitz, we develop new theoretical analysis of Local Fine-grained Attention Sensitivity (LoFAST) quantifying the effect of input feature perturbations on the attention output. Our analysis reveals that the local sensitivity of dot-product self-attention to $\ell_2$ perturbations can actually be controlled by several key quantities associated with the attention weight matrices and the unperturbed input. We empirically validate our theoretical findings by computing non-vacuous certified $\ell_2$-robustness for vision transformers on CIFAR-10 and SVHN datasets. The code for LoFAST is available at https://github.com/AaronHavens/LoFAST.
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https://proceedings.mlr.press/v235/haviv24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/haviv24a/haviv24a.pdf
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https://openreview.net/forum?id=Su0qe33cWA
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Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer
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https://proceedings.mlr.press/v235/haviv24a.html
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Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe’Er
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https://proceedings.mlr.press/v235/haviv24a.html
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ICML 2024
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Optimal transport (OT) and the related Wasserstein metric ($W$) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology.
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https://proceedings.mlr.press/v235/havrilla24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/havrilla24a/havrilla24a.pdf
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https://openreview.net/forum?id=LH6R06NxdB
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GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
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https://proceedings.mlr.press/v235/havrilla24a.html
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Alexander Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Roberta Raileanu
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https://proceedings.mlr.press/v235/havrilla24a.html
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ICML 2024
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State-of-the-art language models can exhibit reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify when and where to refine without access to external feedback. In this paper, we propose Stepwise ORMs (SORMs) which are trained, only on synthetic data, to approximate the expected future reward of the optimal policy or $V^{\star}$ as a form of Process-based reward modeling. Our experiments show that SORMs can more accurately detect incorrect reasoning steps compared to ORMs, thus enabling them to give precise step-level feedback to refinement models. We then train global refinement models, which take only the question and a draft solution as input and predict a corrected solution, and local refinement models which also take as input a critique indicating the location of the first reasoning error. We generate training data for both models synthetically by reusing data used to train the SORM. We find combining global and local refinements, using the ORM as a reranker, significantly outperforms either one individually, as well as a best of three sample baseline. With this strategy we can improve the accuracy of a LLaMA-2 13B model (already fine-tuned with RL) on GSM8K from 53% to 65% when greedily sampled.
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https://proceedings.mlr.press/v235/hayase24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hayase24a/hayase24a.pdf
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https://openreview.net/forum?id=1dtYo5ywXZ
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Understanding MLP-Mixer as a wide and sparse MLP
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https://proceedings.mlr.press/v235/hayase24a.html
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Tomohiro Hayase, Ryo Karakida
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https://proceedings.mlr.press/v235/hayase24a.html
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ICML 2024
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Multi-layer perceptron (MLP) is a fundamental component of deep learning, and recent MLP-based architectures, especially the MLP-Mixer, have achieved significant empirical success. Nevertheless, our understanding of why and how the MLP-Mixer outperforms conventional MLPs remains largely unexplored. In this work, we reveal that sparseness is a key mechanism underlying the MLP-Mixers. First, the Mixers have an effective expression as a wider MLP with Kronecker-product weights, clarifying that the Mixers efficiently embody several sparseness properties explored in deep learning. In the case of linear layers, the effective expression elucidates an implicit sparse regularization caused by the model architecture and a hidden relation to Monarch matrices, which is also known as another form of sparse parameterization. Next, for general cases, we empirically demonstrate quantitative similarities between the Mixer and the unstructured sparse-weight MLPs. Following a guiding principle proposed by Golubeva, Neyshabur and Gur-Ari (2021), which fixes the number of connections and increases the width and sparsity, the Mixers can demonstrate improved performance.
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https://proceedings.mlr.press/v235/hayderi24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hayderi24a/hayderi24a.pdf
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https://openreview.net/forum?id=XlgeQ47Ra9
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MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
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https://proceedings.mlr.press/v235/hayderi24a.html
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Alexandre Hayderi, Amin Saberi, Ellen Vitercik, Anders Wikum
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https://proceedings.mlr.press/v235/hayderi24a.html
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ICML 2024
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Online Bayesian bipartite matching is a central problem in digital marketplaces and exchanges, including advertising, crowdsourcing, ridesharing, and kidney exchange. We introduce a graph neural network (GNN) approach that emulates the problem’s combinatorially-complex optimal online algorithm, which selects actions (e.g., which nodes to match) by computing each action’s value-to-go (VTG)—the expected weight of the final matching if the algorithm takes that action, then acts optimally in the future. We train a GNN to estimate VTG and show empirically that this GNN returns high-weight matchings across a variety of tasks. Moreover, we identify a common family of graph distributions in spatial crowdsourcing applications, such as rideshare, under which VTG can be efficiently approximated by aggregating information within local neighborhoods in the graphs. This structure matches the local behavior of GNNs, providing theoretical justification for our approach.
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https://proceedings.mlr.press/v235/hayou24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hayou24a/hayou24a.pdf
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https://openreview.net/forum?id=NEv8YqBROO
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LoRA+: Efficient Low Rank Adaptation of Large Models
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https://proceedings.mlr.press/v235/hayou24a.html
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Soufiane Hayou, Nikhil Ghosh, Bin Yu
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https://proceedings.mlr.press/v235/hayou24a.html
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ICML 2024
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In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in (Hu et al., 2021) leads to suboptimal finetuning of models with large width. This is due to the fact that adapter matrices A and B in LoRA are updated with the same learning rate in ADAM. Using scaling arguments for large width networks, we demonstrate that the same learning rate does not allow efficient feature learning. We then show that this suboptimality of LoRA can be corrected simply by setting different learning rates for the LoRA adapter matrices A and B with a well-chosen fixed ratio. We call this proposed algorithm LoRA+. In our extensive experiments, LoRA+ improves finetuning speed (up to ∼ 2X SpeedUp) and performance (1% − 2% improvements), at the same computational cost as LoRA. The code is available at https://github.com/nikhil-ghosh-berkeley/loraplus
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https://proceedings.mlr.press/v235/he24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24a/he24a.pdf
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https://openreview.net/forum?id=qkhbyDqlNI
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From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
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https://proceedings.mlr.press/v235/he24a.html
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Jianliang He, Siyu Chen, Fengzhuo Zhang, Zhuoran Yang
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https://proceedings.mlr.press/v235/he24a.html
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ICML 2024
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In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. To this end, consider a hierarchical reinforcement learning (RL) model where the LLM Planner and the Actor perform high-level task planning and low-level execution, respectively. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. Under proper assumptions on the pretraining data, we prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning. Additionally, we highlight the necessity for exploration beyond the subgoals derived from BAIL by proving that naively executing the subgoals returned by LLM leads to a linear regret. As a remedy, we introduce an $\epsilon$-greedy exploration strategy to BAIL, which is proven to incur sublinear regret when the pretraining error is small. Finally, we extend our theoretical framework to include scenarios where the LLM Planner serves as a world model for inferring the transition model of the environment and to multi-agent settings, enabling coordination among multiple Actors.
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https://proceedings.mlr.press/v235/he24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24b/he24b.pdf
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https://openreview.net/forum?id=c2CKmP9l5X
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Deep Neural Room Acoustics Primitive
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https://proceedings.mlr.press/v235/he24b.html
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Yuhang He, Anoop Cherian, Gordon Wichern, Andrew Markham
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https://proceedings.mlr.press/v235/he24b.html
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ICML 2024
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The primary objective of room acoustics is to model the intricate sound propagation dynamics from any source to receiver position within enclosed 3D spaces. These dynamics are encapsulated in the form of a 1D room impulse response (RIR). Precisely measuring RIR is difficult due to the complexity of sound propagation encompassing reflection, diffraction, and absorption. In this work, we propose to learn a continuous neural room acoustics field that implicitly encodes all essential sound propagation primitives for each enclosed 3D space, so that we can infer the RIR corresponding to arbitrary source-receiver positions unseen in the training dataset. Our framework, dubbed DeepNeRAP, is trained in a self-supervised manner without requiring direct access to RIR ground truth that is often needed in prior methods. The key idea is to design two cooperative acoustic agents to actively probe a 3D space, one emitting and the other receiving sound at various locations. Analyzing this sound helps to inversely characterize the acoustic primitives. Our framework is well-grounded in the fundamental physical principles of sound propagation, including reciprocity and globality, and thus is acoustically interpretable and meaningful. We present experiments on both synthetic and real-world datasets, demonstrating superior quality in RIR estimation against closely related methods.
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https://proceedings.mlr.press/v235/he24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24c/he24c.pdf
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https://openreview.net/forum?id=luqH1eL4PN
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Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation
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https://proceedings.mlr.press/v235/he24c.html
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Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Liwei Wang, Jingjing Xu, Zhi Zhang, Hongxia Yang, Di He
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https://proceedings.mlr.press/v235/he24c.html
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ICML 2024
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In this work, we leverage the intrinsic segmentation of language sequences and design a new positional encoding method called Bilevel Positional Encoding (BiPE). For each position, our BiPE blends an intra-segment encoding and an inter-segment encoding. The intra-segment encoding identifies the locations within a segment and helps the model capture the semantic information therein via absolute positional encoding. The inter-segment encoding specifies the segment index, models the relationships between segments, and aims to improve extrapolation capabilities via relative positional encoding. Theoretical analysis shows this disentanglement of positional information makes learning more effective. The empirical results also show that our BiPE has superior length extrapolation capabilities across a wide range of tasks in diverse text modalities.
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https://proceedings.mlr.press/v235/he24d.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24d/he24d.pdf
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https://openreview.net/forum?id=5j7Lq2ASiU
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Distributed Bilevel Optimization with Communication Compression
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https://proceedings.mlr.press/v235/he24d.html
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Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu, Bin Wang, Kun Yuan
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https://proceedings.mlr.press/v235/he24d.html
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ICML 2024
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Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve $10\times$ reduction in communication overhead without severe performance degradation.
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https://proceedings.mlr.press/v235/he24e.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24e/he24e.pdf
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https://openreview.net/forum?id=OI1YP53WKI
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ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation
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https://proceedings.mlr.press/v235/he24e.html
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Nantian He, Shaohui Li, Zhi Li, Yu Liu, You He
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https://proceedings.mlr.press/v235/he24e.html
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ICML 2024
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The diffusion model has demonstrated impressive performance in offline reinforcement learning. However, non-deterministic sampling in diffusion models can lead to unstable performance. Furthermore, the lack of confidence measurements makes it difficult to evaluate the reliability and trustworthiness of the sampled decisions. To address these issues, we present ReDiffuser, which utilizes confidence estimation to ensure reliable decision-making. We achieve this by learning a confidence function based on Random Network Distillation. The confidence function measures the reliability of sampled decisions and contributes to quantitative recognition of reliable decisions. Additionally, we integrate the confidence function into task-specific sampling procedures to realize adaptive-horizon planning and value-embedded planning. Experiments show that the proposed ReDiffuser achieves state-of-the-art performance on standard offline RL datasets.
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https://proceedings.mlr.press/v235/he24f.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24f/he24f.pdf
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https://openreview.net/forum?id=0j28mmQ023
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Domain-wise Data Acquisition to Improve Performance under Distribution Shift
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https://proceedings.mlr.press/v235/he24f.html
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Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui
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https://proceedings.mlr.press/v235/he24f.html
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ICML 2024
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Despite notable progress in enhancing the capability of machine learning against distribution shifts, training data quality remains a bottleneck for cross-distribution generalization. Recently, from a data-centric perspective, there have been considerable efforts to improve model performance through refining the preparation of training data. Inspired by realistic scenarios, this paper addresses a practical requirement of acquiring training samples from various domains on a limited budget to facilitate model generalization to target test domain with distribution shift. Our empirical evidence indicates that the advance in data acquisition can significantly benefit the model performance on shifted data. Additionally, by leveraging unlabeled test domain data, we introduce a Domain-wise Active Acquisition framework. This framework iteratively optimizes the data acquisition strategy as training samples are accumulated, theoretically ensuring the effective approximation of test distribution. Extensive real-world experiments demonstrate our proposal’s advantages in machine learning applications. The code is available at https://github.com/dongbaili/DAA.
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https://proceedings.mlr.press/v235/he24g.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24g/he24g.pdf
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https://openreview.net/forum?id=JApt4Ty89Y
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Quantum Algorithm for Online Exp-concave Optimization
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https://proceedings.mlr.press/v235/he24g.html
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Jianhao He, Chengchang Liu, Xutong Liu, Lvzhou Li, John C.S. Lui
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https://proceedings.mlr.press/v235/he24g.html
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ICML 2024
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We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization with multi-point feedback. We present quantum online quasi-Newton methods to tackle the problem and show that there exists quantum advantages for such problems. Our method approximates the Hessian by quantum estimated inexact gradient and can achieve $O(n\log T)$ regret with $O(1)$ queries at each round, where $n$ is the dimension of the decision set and $T$ is the total decision rounds. Such regret improves the optimal classical algorithm by a factor of $T^{2/3}$.
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https://proceedings.mlr.press/v235/he24h.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24h/he24h.pdf
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https://openreview.net/forum?id=fPwWfoyxL1
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Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity
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https://proceedings.mlr.press/v235/he24h.html
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Chang He, Zhaoye Pan, Xiao Wang, Bo Jiang
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https://proceedings.mlr.press/v235/he24h.html
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ICML 2024
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Optimization problems with access to only zeroth-order information of the objective function on Riemannian manifolds arise in various applications, spanning from statistical learning to robot learning. While various zeroth-order algorithms have been proposed in Euclidean space, they are not inherently designed to handle the challenging constraints imposed by Riemannian manifolds. The proper adaptation of zeroth-order techniques to Riemannian manifolds remained unknown until the pioneering work of (Li et al., 2023a). However, zeroth-order algorithms are widely observed to converge slowly and be unstable in practice. To alleviate these issues, we propose a Riemannian accelerated zeroth-order algorithm with improved robustness. Regarding efficiency, our accelerated algorithm has the function query complexity of $\mathcal{O}(\epsilon^{-7/4}d)$ for finding an $\epsilon$-approximate first-order stationary point. By introducing a small perturbation, it exhibits a function query complexity of $\tilde{\mathcal{O}}(\epsilon^{-7/4}d)$ for seeking a second-order stationary point with a high probability, matching state-of-the-art result in Euclidean space. Moreover, we further establish the almost sure convergence in the asymptotic sense through the Stable Manifold Theorem. Regarding robustness, our algorithm requires larger smoothing parameters in the order of $\tilde{\mathcal{O}}(\epsilon^{7/8}d^{-1/2})$, improving the existing result by a factor of $\tilde{\mathcal{O}}(\epsilon^{3/4})$.
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https://proceedings.mlr.press/v235/he24i.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24i/he24i.pdf
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https://openreview.net/forum?id=qHt8FzPvU9
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The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
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https://proceedings.mlr.press/v235/he24i.html
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Songhua He, Periklis A. Papakonstantinou
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https://proceedings.mlr.press/v235/he24i.html
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ICML 2024
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Deep neural networks (DNNs) have become pivotal in machine learning, but the impact of weight precision, such as in networks with rectified linear units (ReLU), remains underexplored. We analytically investigate the interplay of three key factors: the precision of ReLU network weights, the number of neurons, and the time of the preprocessing algorithm that generates the network description. Our study, which, to the best of our knowledge, is the first formal work on weight precision, yields three main results. (1) We present an exponential time preprocessing algorithm that showcases the possibility of trading ReLU nodes for weight precision. Specifically, our method achieves an exponential reduction in neuron count when computing any function of high complexity with boolean input encoding. What is the implication of the above result in theoretical and practical works? (2) In theory of computing, in general, there is no free lunch. In our case, if you significantly reduce the number of neurons then you should pay the cost in weight precision. To address this, we introduce a notion of network size that considers weight precision in addition to the network’s number of neurons. We establish that under this redefined notion of network size, it is generally impossible to exchange neurons for weight precision in ReLU networks of the same (redefined) size. (3) In practice, we show that high weight precision alone cannot help in reducing the neuron count. If instead of our exponential time preprocessing algorithm one uses any polynomial time algorithm, then it is impossible to non-trivially reduce the neuron count, regardless of the high weight precision.
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https://proceedings.mlr.press/v235/he24j.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24j/he24j.pdf
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https://openreview.net/forum?id=sqv2xP8rfb
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Ambiguity-Aware Abductive Learning
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https://proceedings.mlr.press/v235/he24j.html
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Hao-Yuan He, Hui Sun, Zheng Xie, Ming Li
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https://proceedings.mlr.press/v235/he24j.html
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ICML 2024
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Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertainty, since the knowledge base may be satisfied by numerous potential candidates. This implies that the result of the abduction process, i.e., a set of candidates, is ambiguous; both correct and incorrect candidates are mixed in this set. The prior art of abductive learning selects the candidate that has the minimal inconsistency of the knowledge base. However, this method overlooks the ambiguity in the abduction process and is prone to error when it fails to identify the correct candidates. To address this, we propose Ambiguity-Aware Abductive Learning ($\textrm{A}^3\textrm{BL}$), which evaluates all potential candidates and their probabilities, thus preventing the model from falling into sub-optimal solutions. Both experimental results and theoretical analyses prove that $\textrm{A}^3\textrm{BL}$ markedly enhances ABL by efficiently exploiting the ambiguous abduced supervision.
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https://proceedings.mlr.press/v235/he24k.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24k/he24k.pdf
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https://openreview.net/forum?id=MgTzMaYHvG
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Instruction Tuning for Secure Code Generation
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https://proceedings.mlr.press/v235/he24k.html
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Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin Vechev
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https://proceedings.mlr.press/v235/he24k.html
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ICML 2024
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Modern language models (LMs) have gained widespread acceptance in everyday and professional contexts, particularly in programming. An essential procedure enabling this adoption is instruction tuning, which substantially enhances LMs’ practical utility by training them to follow user instructions and human preferences. However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code. As a result, even the state-of-the-art instruction-tuned LMs frequently produce unsafe code, posing significant security risks. In this work, we introduce SafeCoder to address this gap. SafeCoder performs security-centric fine-tuning using a diverse and high-quality dataset that we collected using an automated pipeline. We integrate the security fine-tuning with standard instruction tuning, to facilitate a joint optimization of both security and utility. Despite its simplicity, we show that SafeCoder is effective across a variety of popular LMs and datasets. It is able to drastically improve security (by about 30%), while preserving utility.
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https://proceedings.mlr.press/v235/he24l.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24l/he24l.pdf
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https://openreview.net/forum?id=S4LqI6CcJ3
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Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning
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https://proceedings.mlr.press/v235/he24l.html
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Zhiyuan He, Yijun Yang, Pin-Yu Chen, Qiang Xu, Tsung-Yi Ho
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https://proceedings.mlr.press/v235/he24l.html
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ICML 2024
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Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs), hindering their use in safety-critical systems. In this paper, we present BEYOND, an innovative AE detection framework designed for reliable predictions. BEYOND identifies AEs by distinguishing the AE’s abnormal relation with its augmented versions, i.e. neighbors, from two prospects: representation similarity and label consistency. An off-the-shelf Self-Supervised Learning (SSL) model is used to extract the representation and predict the label for its highly informative representation capacity compared to supervised learning models. We found clean samples maintain a high degree of representation similarity and label consistency relative to their neighbors, in contrast to AEs which exhibit significant discrepancies. We explain this observation and show that leveraging this discrepancy BEYOND can accurately detect AEs. Additionally, we develop a rigorous justification for the effectiveness of BEYOND. Furthermore, as a plug-and-play model, BEYOND can easily cooperate with the Adversarial Trained Classifier (ATC), achieving state-of-the-art (SOTA) robustness accuracy. Experimental results show that BEYOND outperforms baselines by a large margin, especially under adaptive attacks. Empowered by the robust relationship built on SSL, we found that BEYOND outperforms baselines in terms of both detection ability and speed. Project page: https://huggingface.co/spaces/allenhzy/Be-Your-Own-Neighborhood.
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https://proceedings.mlr.press/v235/he24m.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24m/he24m.pdf
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https://openreview.net/forum?id=fgBWtOw66T
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SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic
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https://proceedings.mlr.press/v235/he24m.html
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Liulu He, Yufei Zhao, Rui Gao, Yuan Du, Li Du
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https://proceedings.mlr.press/v235/he24m.html
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ICML 2024
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Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models. However, these algorithms depend on high-precision arithmetic to maintain inference accuracy, which conflicts with the model quantization. To resolve this conflict and further improve the efficiency of quantized convolution, we proposes SFC, a new algebra transform for fast convolution by extending the Discrete Fourier Transform (DFT) with symbolic computing, in which only additions are required to perform the transformation at specific transform points, avoiding the calculation of irrational number and reducing the requirement for precision. Additionally, we enhance convolution efficiency by introducing correction terms to convert invalid circular convolution outputs of the Fourier method into effective ones. The numerical error analysis is presented for the first time in this type of work and proves that our algorithms can provide a 3.68× multiplication reduction for 3×3 convolution, while the Winograd algorithm only achieves a 2.25× reduction with similarly low numerical errors. Experiments carried out on benchmarks and FPGA show that our new algorithms can further improve the computation efficiency of quantized models while maintaining accuracy, surpassing both the quantization-alone method and existing works on fast convolution quantization.
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https://proceedings.mlr.press/v235/he24n.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24n/he24n.pdf
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https://openreview.net/forum?id=JzWFmMySpn
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Robust Multi-Task Learning with Excess Risks
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https://proceedings.mlr.press/v235/he24n.html
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Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, Han Zhao
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https://proceedings.mlr.press/v235/he24n.html
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ICML 2024
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Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task weights are dynamically adjusted based on their respective losses to prioritize difficult tasks. However, these algorithms face a great challenge whenever label noise is present, in which case excessive weights tend to be assigned to noisy tasks that have relatively large Bayes optimal errors, thereby overshadowing other tasks and causing performance to drop across the board. To overcome this limitation, we propose Multi-Task Learning with Excess Risks (ExcessMTL), an excess risk-based task balancing method that updates the task weights by their distances to convergence instead. Intuitively, ExcessMTL assigns higher weights to worse-trained tasks that are further from convergence. To estimate the excess risks, we develop an efficient and accurate method with Taylor approximation. Theoretically, we show that our proposed algorithm achieves convergence guarantees and Pareto stationarity. Empirically, we evaluate our algorithm on various MTL benchmarks and demonstrate its superior performance over existing methods in the presence of label noise. Our code is available at https://github.com/yifei-he/ExcessMTL.
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https://proceedings.mlr.press/v235/he24o.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24o/he24o.pdf
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https://openreview.net/forum?id=qOMQ0UGLYl
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DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
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https://proceedings.mlr.press/v235/he24o.html
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Kaibo He, Chenhui Zuo, Chengtian Ma, Yanan Sui
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https://proceedings.mlr.press/v235/he24o.html
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ICML 2024
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Learning an effective policy to control high-dimensional, overactuated systems is a significant challenge for deep reinforcement learning algorithms. Such control scenarios are often observed in the neural control of vertebrate musculoskeletal systems. The study of these control mechanisms will provide insights into the control of high-dimensional, overactuated systems. The coordination of actuators, known as muscle synergies in neuromechanics, is considered a presumptive mechanism that simplifies the generation of motor commands. The dynamical structure of a system is the basis of its function, allowing us to derive a synergistic representation of actuators. Motivated by this theory, we propose the Dynamical Synergistic Representation (DynSyn) algorithm. DynSyn aims to generate synergistic representations from dynamical structures and perform task-specific, state-dependent adaptation to the representations to improve motor control. We demonstrate DynSyn’s efficiency across various tasks involving different musculoskeletal models, achieving state-of-the-art sample efficiency and robustness compared to baseline algorithms. DynSyn generates interpretable synergistic representations that capture the essential features of dynamical structures and demonstrates generalizability across diverse motor tasks.
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https://proceedings.mlr.press/v235/hebbar24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hebbar24a/hebbar24a.pdf
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https://openreview.net/forum?id=iLfk2CwEHA
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DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
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https://proceedings.mlr.press/v235/hebbar24a.html
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S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim, Sewoong Oh, Pramod Viswanath
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https://proceedings.mlr.press/v235/hebbar24a.html
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ICML 2024
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Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic. Polar codes, developed on the foundation of Arikan’s polarization kernel, represent the latest breakthrough in coding theory and have emerged as the state-of-the-art error-correction code for short-to-medium block length regimes. In an effort to automate the invention of good channel codes, especially in this regime, we explore a novel, non-linear generalization of Polar codes, which we call DeepPolar codes. DeepPolar codes extend the conventional Polar coding framework by utilizing a larger kernel size and parameterizing these kernels and matched decoders through neural networks. Our results demonstrate that these data-driven codes effectively leverage the benefits of a larger kernel size, resulting in enhanced reliability when compared to both existing neural codes and conventional Polar codes.
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https://proceedings.mlr.press/v235/heilmann24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/heilmann24a/heilmann24a.pdf
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https://openreview.net/forum?id=zc3bAEI5lp
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Differentially Private Sum-Product Networks
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https://proceedings.mlr.press/v235/heilmann24a.html
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Xenia Heilmann, Mattia Cerrato, Ernst Althaus
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https://proceedings.mlr.press/v235/heilmann24a.html
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ICML 2024
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Differentially private ML approaches seek to learn models which may be publicly released while guaranteeing that the input data is kept private. One issue with this construction is that further model releases based on the same training data (e.g. for a new task) incur a further privacy budget cost. Privacy-preserving synthetic data generation is one possible solution to this conundrum. However, models trained on synthetic private data struggle to approach the performance of private, ad-hoc models. In this paper, we present a novel method based on sum-product networks that is able to perform both privacy-preserving classification and privacy-preserving data generation with a single model. To the best of our knowledge, ours is the first approach that provides both discriminative and generative capabilities to differentially private ML. We show that our approach outperforms the state of the art in terms of stability (i.e. number of training runs required for convergence) and utility of the generated data.
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https://proceedings.mlr.press/v235/hemmer24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hemmer24a/hemmer24a.pdf
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https://openreview.net/forum?id=HZyOz9VEg4
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Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
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https://proceedings.mlr.press/v235/hemmer24a.html
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Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz
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https://proceedings.mlr.press/v235/hemmer24a.html
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ICML 2024
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In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any scientific discipline, where we are particularly interested in parsimonious models with a low parameter load. A common strategy here is parameter pruning, removing all parameters with small weights. However, here we find this strategy does not work for DSR, where even low magnitude parameters can contribute considerably to the system dynamics. On the other hand, it is well known that many natural systems which generate complex dynamics, like the brain or ecological networks, have a sparse topology with comparatively few links. Inspired by this, we show that geometric pruning, where in contrast to magnitude-based pruning weights with a low contribution to an attractor’s geometrical structure are removed, indeed manages to reduce parameter load substantially without significantly hampering DSR quality. We further find that the networks resulting from geometric pruning have a specific type of topology, and that this topology, and not the magnitude of weights, is what is most crucial to performance. We provide an algorithm that automatically generates such topologies which can be used as priors for generative modeling of dynamical systems by RNNs, and compare it to other well studied topologies like small-world or scale-free networks.
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https://proceedings.mlr.press/v235/herrmann24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/herrmann24a/herrmann24a.pdf
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https://openreview.net/forum?id=QBj7Uurdwf
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Learning Useful Representations of Recurrent Neural Network Weight Matrices
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https://proceedings.mlr.press/v235/herrmann24a.html
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Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber
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https://proceedings.mlr.press/v235/herrmann24a.html
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ICML 2024
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Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights that facilitate RNN analysis as well as downstream tasks? While the mechanistic approach directly looks at some RNN’s weights to predict its behavior, the functionalist approach analyzes its overall functionality–specifically, its input-output mapping. We consider several mechanistic approaches for RNN weights and adapt the permutation equivariant Deep Weight Space layer for RNNs. Our two novel functionalist approaches extract information from RNN weights by ’interrogating’ the RNN through probing inputs. We develop a theoretical framework that demonstrates conditions under which the functionalist approach can generate rich representations that help determine RNN behavior. We create and release the first two ’model zoo’ datasets for RNN weight representation learning. One consists of generative models of a class of formal languages, and the other one of classifiers of sequentially processed MNIST digits. With the help of an emulation-based self-supervised learning technique we compare and evaluate the different RNN weight encoding techniques on multiple downstream applications. On the most challenging one, namely predicting which exact task the RNN was trained on, functionalist approaches show clear superiority.
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https://proceedings.mlr.press/v235/herrmann24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/herrmann24b/herrmann24b.pdf
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https://openreview.net/forum?id=DprrMz24tk
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Position: Why We Must Rethink Empirical Research in Machine Learning
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https://proceedings.mlr.press/v235/herrmann24b.html
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Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
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https://proceedings.mlr.press/v235/herrmann24b.html
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ICML 2024
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We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.
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https://proceedings.mlr.press/v235/heuillet24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/heuillet24a/heuillet24a.pdf
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https://openreview.net/forum?id=x0vLj1S6Wg
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Randomized Confidence Bounds for Stochastic Partial Monitoring
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https://proceedings.mlr.press/v235/heuillet24a.html
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Maxime Heuillet, Ola Ahmad, Audrey Durand
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https://proceedings.mlr.press/v235/heuillet24a.html
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ICML 2024
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The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback. At each round, a learning agent plays an action while the environment simultaneously chooses an outcome. The agent then observes a feedback signal that is only partially informative about the (unobserved) outcome. The agent leverages the received feedback signals to select actions that minimize the (unobserved) cumulative loss. In contextual PM, the outcomes depend on some side information that is observable by the agent before selecting the action. In this paper, we consider the contextual and non-contextual PM settings with stochastic outcomes. We introduce a new class of PM strategies based on the randomization of deterministic confidence bounds. We also extend regret guarantees to settings where existing stochastic strategies are not applicable. Our experiments show that the proposed RandCBP and RandCBPside* strategies have competitive performance against state-of-the-art baselines in multiple PM games. To illustrate how the PM framework can benefit real world applications, we design a use case on the real-world problem of monitoring the error rate of any deployed classification system.
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https://proceedings.mlr.press/v235/heurtel-depeiges24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/heurtel-depeiges24a/heurtel-depeiges24a.pdf
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https://openreview.net/forum?id=rmEgJ7bhuZ
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Listening to the noise: Blind Denoising with Gibbs Diffusion
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https://proceedings.mlr.press/v235/heurtel-depeiges24a.html
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David Heurtel-Depeiges, Charles Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard
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https://proceedings.mlr.press/v235/heurtel-depeiges24a.html
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ICML 2024
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In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the distribution they model coincide with denoising priors in the Bayesian picture. However, denoising through diffusion-based posterior sampling requires the noise level and covariance to be known, preventing blind denoising. We overcome this limitation by introducing Gibbs Diffusion (GDiff), a general methodology addressing posterior sampling of both the signal and the noise parameters. Assuming arbitrary parametric Gaussian noise, we develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the class of noise distributions, and a Monte Carlo sampler to infer the noise parameters. Our theoretical analysis highlights potential pitfalls, guides diagnostic usage, and quantifies errors in the Gibbs stationary distribution caused by the diffusion model. We showcase our method for 1) blind denoising of natural images involving colored noises with unknown amplitude and exponent, and 2) a cosmology problem, namely the analysis of cosmic microwave background data, where Bayesian inference of "noise" parameters means constraining models of the evolution of the Universe.
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https://proceedings.mlr.press/v235/higuchi24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/higuchi24a/higuchi24a.pdf
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https://openreview.net/forum?id=dkdilv4XD4
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Balanced Resonate-and-Fire Neurons
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https://proceedings.mlr.press/v235/higuchi24a.html
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Saya Higuchi, Sebastian Kairat, Sander Bohte, Sebastian Otte
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https://proceedings.mlr.press/v235/higuchi24a.html
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ICML 2024
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The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.
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https://proceedings.mlr.press/v235/hirono24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hirono24a/hirono24a.pdf
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https://openreview.net/forum?id=AEqim4X0NV
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Understanding Diffusion Models by Feynman’s Path Integral
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https://proceedings.mlr.press/v235/hirono24a.html
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Yuji Hirono, Akinori Tanaka, Kenji Fukushima
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https://proceedings.mlr.press/v235/hirono24a.html
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ICML 2024
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Score-based diffusion models have proven effective in image generation and have gained widespread usage; however, the underlying factors contributing to the performance disparity between stochastic and deterministic (i.e., the probability flow ODEs) sampling schemes remain unclear. We introduce a novel formulation of diffusion models using Feynman’s path integral, which is a formulation originally developed for quantum physics. We find this formulation providing comprehensive descriptions of score-based generative models, and demonstrate the derivation of backward stochastic differential equations and loss functions. The formulation accommodates an interpolating parameter connecting stochastic and deterministic sampling schemes, and we identify this parameter as a counterpart of Planck’s constant in quantum physics. This analogy enables us to apply the Wentzel–Kramers–Brillouin (WKB) expansion, a well-established technique in quantum physics, for evaluating the negative log-likelihood to assess the performance disparity between stochastic and deterministic sampling schemes.
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https://proceedings.mlr.press/v235/hisaki24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hisaki24a/hisaki24a.pdf
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https://openreview.net/forum?id=xB6YJZOKyT
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RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning
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https://proceedings.mlr.press/v235/hisaki24a.html
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Yukinari Hisaki, Isao Ono
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https://proceedings.mlr.press/v235/hisaki24a.html
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ICML 2024
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In this paper, we propose an off-policy deep reinforcement learning (DRL) method utilizing the average reward criterion. While most existing DRL methods employ the discounted reward criterion, this can potentially lead to a discrepancy between the training objective and performance metrics in continuing tasks, making the average reward criterion a recommended alternative. We introduce RVI-SAC, an extension of the state-of-the-art off-policy DRL method, Soft Actor-Critic (SAC), to the average reward criterion. Our proposal consists of (1) Critic updates based on RVI Q-learning, (2) Actor updates introduced by the average reward soft policy improvement theorem, and (3) automatic adjustment of Reset Cost enabling the average reward reinforcement learning to be applied to tasks with termination. We apply our method to the Gymnasium’s Mujoco tasks, a subset of locomotion tasks, and demonstrate that RVI-SAC shows competitive performance compared to existing methods.
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https://proceedings.mlr.press/v235/hoang24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hoang24a/hoang24a.pdf
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https://openreview.net/forum?id=mv9beA1wDF
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Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
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https://proceedings.mlr.press/v235/hoang24a.html
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Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, Trong Nghia Hoang
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https://proceedings.mlr.press/v235/hoang24a.html
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ICML 2024
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Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessitates the use of in silico surrogate functions to predict and maximize the target objective over candidate designs. Although these surrogates can be learned from offline data, their predictions are often inaccurate outside the offline data regime. This challenge raises a fundamental question about the impact of imperfect surrogate model on the performance gap between its optima and the true optima, and to what extent the performance loss can be mitigated. Although prior work developed methods to improve the robustness of surrogate models and their associated optimization processes, a provably quantifiable relationship between an imperfect surrogate and the corresponding performance gap, as well as whether prior methods directly address it, remain elusive. To shed light on this important question, we present a theoretical framework to understand offline black-box optimization, by explicitly bounding the optimization quality based on how well the surrogate matches the latent gradient field that underlines the offline data. Inspired by our theoretical analysis, we propose a principled black-box gradient matching algorithm to create effective surrogate models for offline optimization, improving over prior approaches on various real-world benchmarks.
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https://proceedings.mlr.press/v235/hodgson24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hodgson24a/hodgson24a.pdf
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https://openreview.net/forum?id=qE4nkfyMYl
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Estimating Unknown Population Sizes Using the Hypergeometric Distribution
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https://proceedings.mlr.press/v235/hodgson24a.html
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Liam Hodgson, Danilo Bzdok
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https://proceedings.mlr.press/v235/hodgson24a.html
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ICML 2024
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The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the category sizes are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation problem, even in the presence of severe under-sampling. Our approach accounts for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, as seen in collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and learning an informative latent space. We showcase our method’s versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in reading passage excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.
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https://proceedings.mlr.press/v235/hoffmann24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hoffmann24a/hoffmann24a.pdf
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https://openreview.net/forum?id=HssOwuZiaB
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Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems
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https://proceedings.mlr.press/v235/hoffmann24a.html
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David T Hoffmann, Simon Schrodi, Jelena Bratulić, Nadine Behrmann, Volker Fischer, Thomas Brox
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https://proceedings.mlr.press/v235/hoffmann24a.html
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ICML 2024
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In this work, we study rapid improvements of the training loss in transformers when being confronted with multi-step decision tasks. We found that transformers struggle to learn the intermediate task and both training and validation loss saturate for hundreds of epochs. When transformers finally learn the intermediate task, they do this rapidly and unexpectedly. We call these abrupt improvements Eureka-moments, since the transformer appears to suddenly learn a previously incomprehensible concept. We designed synthetic tasks to study the problem in detail, but the leaps in performance can be observed also for language modeling and in-context learning (ICL). We suspect that these abrupt transitions are caused by the multi-step nature of these tasks. Indeed, we find connections and show that ways to improve on the synthetic multi-step tasks can be used to improve the training of language modeling and ICL. Using the synthetic data we trace the problem back to the Softmax function in the self-attention block of transformers and show ways to alleviate the problem. These fixes reduce the required number of training steps, lead to higher likelihood to learn the intermediate task, to higher final accuracy and training becomes more robust to hyper-parameters.
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https://proceedings.mlr.press/v235/hogg24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hogg24a/hogg24a.pdf
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https://openreview.net/forum?id=rU8o0QQCy0
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Position: Is machine learning good or bad for the natural sciences?
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https://proceedings.mlr.press/v235/hogg24a.html
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David W Hogg, Soledad Villar
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https://proceedings.mlr.press/v235/hogg24a.html
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ICML 2024
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Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology — in which only the data exist — and a strong epistemology — in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they amplify confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics.
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https://proceedings.mlr.press/v235/hogsgaard24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hogsgaard24a/hogsgaard24a.pdf
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https://openreview.net/forum?id=ufgVvFmUom
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Sparse Dimensionality Reduction Revisited
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https://proceedings.mlr.press/v235/hogsgaard24a.html
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Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn
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https://proceedings.mlr.press/v235/hogsgaard24a.html
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ICML 2024
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The sparse Johnson-Lindenstrauss transform is one of the central techniques in dimensionality reduction. It supports embedding a set of $n$ points in $\mathbb{R}^d$ into $m=O(\varepsilon^{-2} \ln n)$ dimensions while preserving all pairwise distances to within $1 \pm \varepsilon$. Each input point $x$ is embedded to $Ax$, where $A$ is an $m \times d$ matrix having $s$ non-zeros per column, allowing for an embedding time of $O(s \|x\|_0)$. Since the sparsity of $A$ governs the embedding time, much work has gone into improving the sparsity $s$. The current state-of-the-art by Kane and Nelson (2014) shows that $s = O(\varepsilon^{-1} \ln n)$ suffices. This is almost matched by a lower bound of $s = \Omega(\varepsilon^{-1} \ln n/\ln(1/\varepsilon))$ by Nelson and Nguyen (2013) for $d=\Omega(n)$. Previous work thus suggests that we have near-optimal embeddings. In this work, we revisit sparse embeddings and present a sparser embedding for instances in which $d = n^{o(1)}$, which in many applications is realistic. Formally, our embedding achieves $s = O(\varepsilon^{-1}(\ln n/\ln(1/\varepsilon)+\ln^{2/3}n \ln^{1/3} d))$. We also complement our analysis by strengthening the lower bound of Nelson and Nguyen to hold also when $d \ll n$, thereby matching the first term in our new sparsity upper bound. Finally, we also improve the sparsity of the best oblivious subspace embeddings for optimal embedding dimensionality.
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https://proceedings.mlr.press/v235/hoier24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hoier24a/hoier24a.pdf
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https://openreview.net/forum?id=ui8ewXg1hV
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Two Tales of Single-Phase Contrastive Hebbian Learning
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https://proceedings.mlr.press/v235/hoier24a.html
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Rasmus Høier, Christopher Zach
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https://proceedings.mlr.press/v235/hoier24a.html
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ICML 2024
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The search for "biologically plausible" learning algorithms has converged on the idea of representing gradients as activity differences. However, most approaches require a high degree of synchronization (distinct phases during learning) and introduce substantial computational overhead, which raises doubts regarding their biological plausibility as well as their potential utility for neuromorphic computing. Furthermore, they commonly rely on applying infinitesimal perturbations (nudges) to output units, which is impractical in noisy environments. Recently it has been shown that by modelling artificial neurons as dyads with two oppositely nudged compartments, it is possible for a fully local learning algorithm named “dual propagation” to bridge the performance gap to backpropagation, without requiring separate learning phases or infinitesimal nudging. However, the algorithm has the drawback that its numerical stability relies on symmetric nudging, which may be restrictive in biological and analog implementations. In this work we first provide a solid foundation for the objective underlying the dual propagation method, which also reveals a surpising connection with adversarial robustness. Second, we demonstrate how dual propagation is related to a particular adjoint state method, which is stable regardless of asymmetric nudging.
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https://proceedings.mlr.press/v235/hojny24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hojny24a/hojny24a.pdf
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https://openreview.net/forum?id=nAoiUlz4Bf
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Verifying message-passing neural networks via topology-based bounds tightening
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https://proceedings.mlr.press/v235/hojny24a.html
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Christopher Hojny, Shiqiang Zhang, Juan S Campos, Ruth Misener
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https://proceedings.mlr.press/v235/hojny24a.html
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ICML 2024
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Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural networks (MPNNs) using a Rectified Linear Unit (ReLU) activation function. Because our work builds on mixed-integer optimization, it encodes a wide variety of subproblems, for example it admits (i) both adding and removing edges, (ii) both global and local budgets, and (iii) both topological perturbations and feature modifications. Our key technology, topology-based bounds tightening, uses graph structure to tighten bounds. We also experiment with aggressive bounds tightening to dynamically change the optimization constraints by tightening variable bounds. To demonstrate the effectiveness of these strategies, we implement an extension to the open-source branch-and-cut solver SCIP. We test on both node and graph classification problems and consider topological attacks that both add and remove edges.
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https://proceedings.mlr.press/v235/holl24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/holl24a/holl24a.pdf
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https://openreview.net/forum?id=4oD0tRrUOX
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$\bfΦ_\textrmFlow$: Differentiable Simulations for PyTorch, TensorFlow and Jax
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https://proceedings.mlr.press/v235/holl24a.html
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Philipp Holl, Nils Thuerey
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https://proceedings.mlr.press/v235/holl24a.html
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ICML 2024
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Differentiable processes have proven an invaluable tool for machine learning (ML) in scientific and engineering settings, but most ML libraries are not primarily designed for such applications. We present $\Phi_\textrm{Flow}$, a Python toolkit that seamlessly integrates with PyTorch, TensorFlow, Jax and NumPy, simplifying the process of writing differentiable simulation code at every step. $\Phi_\textrm{Flow}$ provides many essential features that go beyond the capabilities of the base libraries, such as differential operators, boundary conditions, the ability to write dimensionality-agnostic code, floating-point precision management, fully differentiable preconditioned (sparse) linear solves, automatic matrix generation via function tracing, integration of SciPy optimizers, simulation vectorization, and visualization tools. At the same time, $\Phi_\textrm{Flow}$ inherits all important traits of the base ML libraries, such as GPU / TPU support, just-in-time compilation, and automatic differentiation. Put together, these features drastically simplify scientific code like PDE or ODE solvers on grids or unstructured meshes, and $\Phi_\textrm{Flow}$ even includes out-of-the-box support for fluid simulations. $\Phi_\textrm{Flow}$ has been used in various publications and as a ground-truth solver in multiple scientific data sets.
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https://proceedings.mlr.press/v235/holland24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/holland24a/holland24a.pdf
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https://openreview.net/forum?id=WVORGH73Cg
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Criterion Collapse and Loss Distribution Control
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https://proceedings.mlr.press/v235/holland24a.html
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Matthew J. Holland
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https://proceedings.mlr.press/v235/holland24a.html
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ICML 2024
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In this work, we consider the notion of "criterion collapse," in which optimization of one metric implies optimality in another, with a particular focus on conditions for collapse into error probability minimizers under a wide variety of learning criteria, ranging from DRO and OCE risks (CVaR, tilted ERM) to non-monotonic criteria underlying recent ascent-descent algorithms explored in the literature (Flooding, SoftAD). We show how collapse in the context of losses with a Bernoulli distribution goes far beyond existing results for CVaR and DRO, then expand our scope to include surrogate losses, showing conditions where monotonic criteria such as tilted ERM cannot avoid collapse, whereas non-monotonic alternatives can.
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https://proceedings.mlr.press/v235/holstege24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/holstege24a/holstege24a.pdf
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https://openreview.net/forum?id=L4ERlHrJRT
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Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
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https://proceedings.mlr.press/v235/holstege24a.html
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Floris Holstege, Bram Wouters, Noud Van Giersbergen, Cees Diks
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https://proceedings.mlr.press/v235/holstege24a.html
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ICML 2024
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An important challenge in the field of interpretable machine learning is to ensure that deep neural networks (DNNs) use the correct or desirable input features in performing their tasks. Concept-removal methods aim to do this by eliminating concepts that are spuriously correlated with the main task from the neural network representation of the data. However, existing methods tend to be overzealous by inadvertently removing part of the correct or desirable features as well, leading to wrong interpretations and hurting model performance. We propose an iterative algorithm that separates spurious from main-task concepts by jointly estimating two low-dimensional orthogonal subspaces of the neural network representation. By evaluating the algorithm on benchmark datasets from computer vision (Waterbirds, CelebA) and natural language processing (MultiNLI), we show it outperforms existing concept-removal methods in terms of identifying the main-task and spurious concepts, and removing only the latter.
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https://proceedings.mlr.press/v235/hong24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24a/hong24a.pdf
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https://openreview.net/forum?id=e3Dpq3WdMv
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Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
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https://proceedings.mlr.press/v235/hong24a.html
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Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li
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https://proceedings.mlr.press/v235/hong24a.html
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ICML 2024
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Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected. This study conducts the first, thorough evaluation of three (3) leading LLMs using five (5) SoTA compression techniques across eight (8) trustworthiness dimensions. Our experiments highlight the intricate interplay between compression and trustworthiness, revealing some interesting patterns. We find that quantization is currently a more effective approach than pruning in achieving efficiency and trustworthiness simultaneously. For instance, a 4-bit quantized model retains the trustworthiness of its original counterpart, but model pruning significantly degrades trustworthiness, even at 50% sparsity. Moreover, employing quantization within a moderate bit range could unexpectedly improve certain trustworthiness dimensions such as ethics and fairness. Conversely, extreme quantization to very low bit levels (3 bits) tends to reduce trustworthiness significantly. This increased risk cannot be uncovered by looking at benign performance alone, in turn, mandating comprehensive trustworthiness evaluation in practice. These findings culminate in practical recommendations for simultaneously achieving high utility, efficiency, and trustworthiness in LLMs. Code and models are available at https://decoding-comp-trust.github.io.
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https://proceedings.mlr.press/v235/hong24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24b/hong24b.pdf
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https://openreview.net/forum?id=DN7uk4gQ7C
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Enhancing Sufficient Dimension Reduction via Hellinger Correlation
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https://proceedings.mlr.press/v235/hong24b.html
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Seungbeom Hong, Ilmun Kim, Jun Song
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https://proceedings.mlr.press/v235/hong24b.html
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ICML 2024
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In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based on conditional independence. Our work is primarily motivated by the recent introduction of the Hellinger correlation as a dependency measure. Utilizing this measure, we have developed a method capable of effectively detecting the dimension reduction subspace, complete with theoretical justification. Through extensive numerical experiments, we demonstrate that our proposed method significantly enhances and outperforms existing SDR methods. This improvement is largely attributed to our proposed method’s deeper understanding of data dependencies and the refinement of existing SDR techniques.
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https://proceedings.mlr.press/v235/hong24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24c/hong24c.pdf
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https://openreview.net/forum?id=5QWKec0eDF
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Diversified Batch Selection for Training Acceleration
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https://proceedings.mlr.press/v235/hong24c.html
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Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor Tsang, Yanfeng Wang
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https://proceedings.mlr.press/v235/hong24c.html
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ICML 2024
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The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption. To save cost, a prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. Although recent efforts achieve advancements by measuring the impact of each sample on generalization, their reliance on additional reference models inherently limits their practical applications, when there are no such ideal models available. On the other hand, the vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner, which sacrifices the diversity and induces the redundancy. To tackle this dilemma, we propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples. Specifically, we define a novel selection objective that measures the group-wise orthogonalized representativeness to combat the redundancy issue of previous sample-wise criteria, and provide a principled selection-efficient realization. Extensive experiments across various tasks demonstrate the significant superiority of DivBS in the performance-speedup trade-off. The code is publicly available.
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https://proceedings.mlr.press/v235/hong24d.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24d/hong24d.pdf
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https://openreview.net/forum?id=DlR8fWgJRl
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Model-based Reinforcement Learning for Confounded POMDPs
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https://proceedings.mlr.press/v235/hong24d.html
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Mao Hong, Zhengling Qi, Yanxun Xu
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https://proceedings.mlr.press/v235/hong24d.html
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ICML 2024
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We propose a model-based offline reinforcement learning (RL) algorithm for confounded partially observable Markov decision processes (POMDPs) under general function approximations and show it is provably efficient under some technical conditions such as the partial coverage imposed on the offline data distribution. Specifically, we first establish a novel model-based identification result for learning the effect of any action on the reward and future transitions in the confounded POMDP. Using this identification result, we then design a nonparametric two-stage estimation procedure to construct an estimator for off-policy evaluation (OPE), which permits general function approximations. Finally, we learn the optimal policy by performing a conservative policy optimization within the confidence regions based on the proposed estimation procedure for OPE. Under some mild conditions, we establish a finite-sample upper bound on the suboptimality of the learned policy in finding the optimal one, which depends on the sample size and the length of horizons polynomially.
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https://proceedings.mlr.press/v235/hong24e.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24e/hong24e.pdf
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https://openreview.net/forum?id=S80a4hJtuE
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A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
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https://proceedings.mlr.press/v235/hong24e.html
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Kihyuk Hong, Ambuj Tewari
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https://proceedings.mlr.press/v235/hong24e.html
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ICML 2024
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We study offline reinforcement learning (RL) with linear MDPs under the infinite-horizon discounted setting which aims to learn a policy that maximizes the expected discounted cumulative reward using a pre-collected dataset. Existing algorithms for this setting either require a uniform data coverage assumptions or are computationally inefficient for finding an $\epsilon$-optimal policy with $\mathcal{O}(\epsilon^{-2})$ sample complexity. In this paper, we propose a primal dual algorithm for offline RL with linear MDPs in the infinite-horizon discounted setting. Our algorithm is the first computationally efficient algorithm in this setting that achieves sample complexity of $\mathcal{O}(\epsilon^{-2})$ with partial data coverage assumption. Our work is an improvement upon a recent work that requires $\mathcal{O}(\epsilon^{-4})$ samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals.
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https://proceedings.mlr.press/v235/hooda24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hooda24a/hooda24a.pdf
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https://openreview.net/forum?id=ADnUzsmsLW
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Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates
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https://proceedings.mlr.press/v235/hooda24a.html
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Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha
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https://proceedings.mlr.press/v235/hooda24a.html
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ICML 2024
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Large Language Models’ success in text generation has also made them better at code generation and coding tasks. While a lot of work has demonstrated their remarkable performance on tasks such as code completion and editing, it is still unclear as to why. We help bridge this gap by exploring to what degree auto-regressive models understand the logical constructs of the underlying programs. We propose Counterfactual Analysis for Programming Concept Predicates (CACP) as a counterfactual testing framework to evaluate whether Large Code Models understand programming concepts. With only black-box access to the model, we use CACP to evaluate ten popular Large Code Models for four different programming concepts. Our findings suggest that current models lack understanding of concepts such as data flow and control flow.
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https://proceedings.mlr.press/v235/hordan24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hordan24a/hordan24a.pdf
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https://openreview.net/forum?id=ApRKrKZJSk
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Weisfeiler Leman for Euclidean Equivariant Machine Learning
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https://proceedings.mlr.press/v235/hordan24a.html
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Snir Hordan, Tal Amir, Nadav Dym
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https://proceedings.mlr.press/v235/hordan24a.html
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ICML 2024
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The $k$-Weisfeiler-Leman ($k$-WL) graph isomorphism test hierarchy is a common method for assessing the expressive power of graph neural networks (GNNs). Recently, GNNs whose expressive power is equivalent to the $2$-WL test were proven to be universal on weighted graphs which encode $3\mathrm{D}$ point cloud data, yet this result is limited to invariant continuous functions on point clouds. In this paper, we extend this result in three ways: Firstly, we show that PPGN can simulate $2$-WL uniformly on all point clouds with low complexity. Secondly, we show that $2$-WL tests can be extended to point clouds which include both positions and velocities, a scenario often encountered in applications. Finally, we provide a general framework for proving equivariant universality and leverage it to prove that a simple modification of this invariant PPGN architecture can be used to obtain a universal equivariant architecture that can approximate all continuous equivariant functions uniformly. Building on our results, we develop our WeLNet architecture, which sets new state-of-the-art results on the N-Body dynamics task and the GEOM-QM9 molecular conformation generation task.
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https://proceedings.mlr.press/v235/horie24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/horie24a/horie24a.pdf
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https://openreview.net/forum?id=WajJf47TUi
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Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
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https://proceedings.mlr.press/v235/horie24a.html
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Masanobu Horie, Naoto Mitsume
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https://proceedings.mlr.press/v235/horie24a.html
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ICML 2024
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Utilizing machine learning to address partial differential equations (PDEs) presents significant challenges due to the diversity of spatial domains and their corresponding state configurations, which complicates the task of encompassing all potential scenarios through data-driven methodologies alone. Moreover, there are legitimate concerns regarding the generalization and reliability of such approaches, as they often overlook inherent physical constraints. In response to these challenges, this study introduces a novel machine-learning architecture that is highly generalizable and adheres to conservation laws and physical symmetries, thereby ensuring greater reliability. The foundation of this architecture is graph neural networks (GNNs), which are adept at accommodating a variety of shapes and forms. Additionally, we explore the parallels between GNNs and traditional numerical solvers, facilitating a seamless integration of conservative principles and symmetries into machine learning models. Our findings from experiments demonstrate that the model’s inclusion of physical laws significantly enhances its generalizability, i.e., no significant accuracy degradation for unseen spatial domains while other models degrade. The code is available at https://github.com/yellowshippo/fluxgnn-icml2024.
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https://proceedings.mlr.press/v235/horoi24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/horoi24a/horoi24a.pdf
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https://openreview.net/forum?id=hLuNVjRnY3
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Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
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https://proceedings.mlr.press/v235/horoi24a.html
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Stefan Horoi, Albert Manuel Orozco Camacho, Eugene Belilovsky, Guy Wolf
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https://proceedings.mlr.press/v235/horoi24a.html
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ICML 2024
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Combining the predictions of multiple trained models through ensembling is generally a good way to improve accuracy by leveraging the different learned features of the models, however it comes with high computational and storage costs. Model fusion, the act of merging multiple models into one by combining their parameters reduces these costs but doesn’t work as well in practice. Indeed, neural network loss landscapes are high-dimensional and non-convex and the minima found through learning are typically separated by high loss barriers. Numerous recent works have been focused on finding permutations matching one network features to the features of a second one, lowering the loss barrier on the linear path between them in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models’ neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our alignment method leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder setting where more than 2 models are merged, and we find that CCA Merge works significantly better than past methods. Our code is publicly available at https://github.com/shoroi/align-n-merge
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https://proceedings.mlr.press/v235/horowitz24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/horowitz24a/horowitz24a.pdf
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https://openreview.net/forum?id=q3Bz1TVTq4
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Classification Under Strategic Self-Selection
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https://proceedings.mlr.press/v235/horowitz24a.html
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Guy Horowitz, Yonatan Sommer, Moran Koren, Nir Rosenfeld
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https://proceedings.mlr.press/v235/horowitz24a.html
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ICML 2024
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When users stand to gain from certain predictive outcomes, they are prone to act strategically to obtain predictions that are favorable. Most current works consider strategic behavior that manifests as users modifying their features; instead, we study a novel setting in which users decide whether to even participate (or not), this in response to the learned classifier. Considering learning approaches of increasing strategic awareness, we investigate the effects of user self-selection on learning, and the implications of learning on the composition of the self-selected population. Building on this, we propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that complement our analysis and demonstrate the utility of our approach.
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https://proceedings.mlr.press/v235/horvath24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/horvath24a/horvath24a.pdf
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https://openreview.net/forum?id=7bjyambg4x
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Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
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https://proceedings.mlr.press/v235/horvath24a.html
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Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang
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https://proceedings.mlr.press/v235/horvath24a.html
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ICML 2024
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Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years, ranging from self-driving cars to intelligent assistants. However, these models have been getting increasingly large as they become more accurate and safe. This means that their training becomes increasingly costly and time-consuming, and typically yields a single model to fit all targets. To mitigate this, various techniques have been proposed in the literature, including pruning, sparsification or quantization of the model weights and updates. While achieving high compression rates, they often incur significant computational overheads at training or lead to non-negligible accuracy penalty. Alternatively, factorization methods have been leveraged for low-rank compression of DNNs. Similarly, such techniques (e.g., SVD) frequently rely on heavy iterative decompositions of layers and are potentially sub-optimal for non-linear models, such as DNNs. We take a further step in designing efficient low-rank models and propose Maestro, a framework for trainable low-rank layers. Instead of iteratively applying a priori decompositions, the low-rank structure is baked into the training process through LoD, a low-rank ordered decomposition. Not only is this the first time importance ordering via sampling is applied on the decomposed DNN structure, but it also allows selecting ranks at a layer granularity. Our theoretical analysis demonstrates that LoD recovers the SVD decomposition of linear mapping on uniformly distributed data and PCA for linear autoencoders. Applied to DNNs, Maestro enables the extraction of lower footprint models that preserve performance. Simultaneously, it enables the graceful tradeoff between accuracy-latency for deployment to even more constrained devices, without retraining.
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https://proceedings.mlr.press/v235/horwitz24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/horwitz24a/horwitz24a.pdf
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https://openreview.net/forum?id=761UxjOTHB
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Recovering the Pre-Fine-Tuning Weights of Generative Models
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https://proceedings.mlr.press/v235/horwitz24a.html
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Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen
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https://proceedings.mlr.press/v235/horwitz24a.html
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ICML 2024
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The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning. This practice is considered safe, as no current method can recover the unsafe, pre-fine-tuning model weights. In this paper, we demonstrate that this assumption is often false. Concretely, we present Spectral DeTuning, a method that can recover the weights of the pre-fine-tuning model using a few low-rank (LoRA) fine-tuned models. In contrast to previous attacks that attempt to recover pre-fine-tuning capabilities, our method aims to recover the exact pre-fine-tuning weights. Our approach exploits this new vulnerability against large-scale models such as a personalized Stable Diffusion and an aligned Mistral. The code is available at https://vision.huji.ac.il/spectral_detuning/.
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https://proceedings.mlr.press/v235/hossain24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hossain24a/hossain24a.pdf
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https://openreview.net/forum?id=S2XgbBCJy0
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Equilibrium of Data Markets with Externality
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https://proceedings.mlr.press/v235/hossain24a.html
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Safwan Hossain, Yiling Chen
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https://proceedings.mlr.press/v235/hossain24a.html
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ICML 2024
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We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous game. A key component here is the negative externality buyers induce on one another due to data purchases. Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality. While the outcomes are bleak without any intervention, mirroring the limitations of current data markets, we prove that for a standard class of externality functions, platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees. We next consider a more realistic setting where buyers learn their valuations over time through market interactions. Our intervention is feasible here as well, and we consider learning algorithms to achieve low regret concerning both individual and cumulative utility metrics. Lastly, we analyze the promises of this intervention under a much richer externality model.
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https://proceedings.mlr.press/v235/hossain24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hossain24b/hossain24b.pdf
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https://openreview.net/forum?id=UIxOkdBmxh
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A Persuasive Approach to Combating Misinformation
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https://proceedings.mlr.press/v235/hossain24b.html
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Safwan Hossain, Andjela Mladenovic, Yiling Chen, Gauthier Gidel
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https://proceedings.mlr.press/v235/hossain24b.html
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ICML 2024
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Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user’s future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting.
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https://proceedings.mlr.press/v235/hossain24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hossain24c/hossain24c.pdf
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https://openreview.net/forum?id=8JFIKpzumn
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Multi-Sender Persuasion: A Computational Perspective
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https://proceedings.mlr.press/v235/hossain24c.html
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Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes, Haifeng Xu
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https://proceedings.mlr.press/v235/hossain24c.html
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ICML 2024
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We consider multiple senders with informational advantage signaling to convince a single self-interested actor to take certain actions. Generalizing the seminal Bayesian Persuasion framework, such settings are ubiquitous in computational economics, multi-agent learning, and machine learning with multiple objectives. The core solution concept here is the Nash equilibrium of senders’ signaling policies. Theoretically, we prove that finding an equilibrium in general is PPAD-Hard; in fact, even computing a sender’s best response is NP-Hard. Given these intrinsic difficulties, we turn to finding local Nash equilibria. We propose a novel differentiable neural network to approximate this game’s non-linear and discontinuous utilities. Complementing this with the extra-gradient algorithm, we discover local equilibria that Pareto dominates full-revelation equilibria and those found by existing neural networks. Broadly, our theoretical and empirical contributions are of interest to a large class of economic problems.
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https://proceedings.mlr.press/v235/hotti24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hotti24a/hotti24a.pdf
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https://openreview.net/forum?id=Grrydzui3A
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Efficient Mixture Learning in Black-Box Variational Inference
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https://proceedings.mlr.press/v235/hotti24a.html
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Alexandra Hotti, Oskar Kviman, Ricky Molén, Vı́ctor Elvira, Jens Lagergren
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https://proceedings.mlr.press/v235/hotti24a.html
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ICML 2024
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Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling the number of mixture components can lead to a linear increase in the number of learnable parameters and a quadratic increase in inference time due to the evaluation of the evidence lower bound (ELBO). Our two key contributions address these limitations. First, we introduce the novel Multiple Importance Sampling Variational Autoencoder (MISVAE), which amortizes the mapping from input to mixture-parameter space using one-hot encodings. Fortunately, with MISVAE, each additional mixture component incurs a negligible increase in network parameters. Second, we construct two new estimators of the ELBO for mixtures in BBVI, enabling a tremendous reduction in inference time with marginal or even improved impact on performance. Collectively, our contributions enable scalability to hundreds of mixture components and provide superior estimation performance in shorter time, with fewer network parameters compared to previous Mixture VAEs. Experimenting with MISVAE, we achieve astonishing, SOTA results on MNIST. Furthermore, we empirically validate our estimators in other BBVI settings, including Bayesian phylogenetic inference, where we improve inference times for the SOTA mixture model on eight data sets.
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https://proceedings.mlr.press/v235/hou24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hou24a/hou24a.pdf
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https://openreview.net/forum?id=YCzbfs2few
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IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency
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https://proceedings.mlr.press/v235/hou24a.html
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Linshan Hou, Ruili Feng, Zhongyun Hua, Wei Luo, Leo Yu Zhang, Yiming Li
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https://proceedings.mlr.press/v235/hou24a.html
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ICML 2024
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Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a ‘firewall’ to filter out malicious testing images. Our method is motivated by an intriguing phenomenon, i.e., parameter-oriented scaling consistency (PSC), where the prediction confidences of poisoned samples are significantly more consistent than those of benign ones when amplifying model parameters. In particular, we provide theoretical analysis to safeguard the foundations of the PSC phenomenon. We also design an adaptive method to select BN layers to scale up for effective detection. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our IBD-PSC method and its resistance to adaptive attacks. Codes are available at https://github.com/THUYimingLi/BackdoorBox.
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https://proceedings.mlr.press/v235/hou24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hou24b/hou24b.pdf
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https://openreview.net/forum?id=byxXa99PtF
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Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
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https://proceedings.mlr.press/v235/hou24b.html
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Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, Yang Zhang
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https://proceedings.mlr.press/v235/hou24b.html
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ICML 2024
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Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model’s training data. In large language models (LLMs) specifically, identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability, but remains an important open research question. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling, which can be applied to any pre-trained LLM. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions. We show that, when aleatoric uncertainty arises from ambiguity or under-specification in LLM inputs, this approach makes it possible to factor an (un-clarified) LLM’s predictions into separate aleatoric and epistemic terms, using a decomposition similar to the one employed by Bayesian neural networks. Empirical evaluations demonstrate that input clarification ensembling provides accurate and reliable uncertainty quantification on several language processing tasks. Code and data are available at https://github.com/UCSB-NLP-Chang/llm_uncertainty.
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https://proceedings.mlr.press/v235/hou24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hou24c/hou24c.pdf
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https://openreview.net/forum?id=3WCvnkHnxV
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PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
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https://proceedings.mlr.press/v235/hou24c.html
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Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, Daniel Lazar
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https://proceedings.mlr.press/v235/hou24c.html
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ICML 2024
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On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several drawbacks: (1) most user devices are too small to train large models on-device, (2) on-device training is communication- and computation-intensive, and (3) on-device training can be difficult to debug and deploy. To address these problems, we propose Private Evolution-Text (PrE-Text), a method for generating differentially private (DP) synthetic textual data. First, we show that across multiple datasets, training small models (models that fit on user devices) with PrE-Text synthetic data outperforms small models trained on-device under practical privacy regimes ($\epsilon=1.29$, $\epsilon=7.58$). We achieve these results while using 9$\times$ fewer rounds, 6$\times$ less client computation per round, and 100$\times$ less communication per round. Second, finetuning large models on PrE-Text’s DP synthetic data improves large language model (LLM) performance on private data across the same range of privacy budgets. Altogether, these results suggest that training on DP synthetic data can be a better option than training a model on-device on private distributed data. Code is available at https://github.com/houcharlie/PrE-Text.
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https://proceedings.mlr.press/v235/hounie24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hounie24a/hounie24a.pdf
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https://openreview.net/forum?id=9CCoVyFuEp
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Loss Shaping Constraints for Long-Term Time Series Forecasting
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https://proceedings.mlr.press/v235/hounie24a.html
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Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
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https://proceedings.mlr.press/v235/hounie24a.html
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ICML 2024
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Several applications in time series forecasting require predicting multiple steps ahead. Despite the vast amount of literature in the topic, both classical and recent deep learning based approaches have mostly focused on minimising performance averaged over the predicted window. We observe that this can lead to disparate distributions of errors across forecasting steps, especially for recent transformer architectures trained on popular forecasting benchmarks. That is, optimising performance on average can lead to undesirably large errors at specific time-steps. In this work, we present a Constrained Learning approach for long-term time series forecasting that aims to find the best model in terms of average performance that respects a user-defined upper bound on the loss at each time-step. We call our approach loss shaping constraints because it imposes constraints on the loss at each time step, and leverage recent duality results to show that despite its non-convexity, the resulting problem has a bounded duality gap. We propose a practical primal-dual algorithm to tackle it, and demonstrate that the proposed approach exhibits competitive average performance in time series forecasting benchmarks, while shaping the distribution of errors across the predicted window.
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https://proceedings.mlr.press/v235/hsieh24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hsieh24a/hsieh24a.pdf
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https://openreview.net/forum?id=IgwtflILyj
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Careful with that Scalpel: Improving Gradient Surgery with an EMA
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https://proceedings.mlr.press/v235/hsieh24a.html
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Yu-Guan Hsieh, James Thornton, Eugene Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin
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https://proceedings.mlr.press/v235/hsieh24a.html
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ICML 2024
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Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. performance on another dataset, robustness, agreement with a prior). Although the simplest approach to incorporating an auxiliary loss is to sum it with the training loss as a regularizer, recent works have shown that one can improve performance by blending the gradients beyond a simple sum; this is known as gradient surgery. We cast the problem as a constrained minimization problem where the auxiliary objective is minimized among the set of minimizers of the training loss. To solve this bilevel problem, we follow a parameter update direction that combines the training loss gradient and the orthogonal projection of the auxiliary gradient to the training gradient. In a setting where gradients come from mini-batches, we explain how, using a moving average of the training loss gradients, we can carefully maintain this critical orthogonality property. We demonstrate that our method, Bloop, can lead to much better performances on NLP and vision experiments than other gradient surgery methods without EMA.
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https://proceedings.mlr.press/v235/hsu24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hsu24a/hsu24a.pdf
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https://openreview.net/forum?id=0iXp5P77ho
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Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
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https://proceedings.mlr.press/v235/hsu24a.html
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Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns, Chelsea Finn, Jiajun Wu
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https://proceedings.mlr.press/v235/hsu24a.html
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ICML 2024
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Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set. In this work, we consider endowing a neural network autoencoder with three select inductive biases from the literature: data compression into a grid-like latent space via quantization, collective independence amongst latents, and minimal functional influence of any latent on how other latents determine data generation. In principle, these inductive biases are deeply complementary: they most directly specify properties of the latent space, encoder, and decoder, respectively. In practice, however, naively combining existing techniques instantiating these inductive biases fails to yield significant benefits. To address this, we propose adaptations to the three techniques that simplify the learning problem, equip key regularization terms with stabilizing invariances, and quash degenerate incentives. The resulting model, Tripod, achieves state-of-the-art results on a suite of four image disentanglement benchmarks. We also verify that Tripod significantly improves upon its naive incarnation and that all three of its "legs" are necessary for best performance.
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https://proceedings.mlr.press/v235/hu24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24a/hu24a.pdf
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https://openreview.net/forum?id=kLiDMGJKx1
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Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
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https://proceedings.mlr.press/v235/hu24a.html
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Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Haozheng Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, Han Liu
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https://proceedings.mlr.press/v235/hu24a.html
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ICML 2024
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We introduce an Outlier-Efficient Modern Hopfield Model (termed OutEffHop) and use it to address the outlier inefficiency problem of training gigantic transformer-based models. Our main contribution is a novel associative memory model facilitating outlier-efficient associative memory retrievals. Interestingly, this memory model manifests a model-based interpretation of an outlier-efficient attention mechanism (Softmax_1): it is an approximation of the memory retrieval process of OutEffHop. Methodologically, this allows us to introduce novel outlier-efficient Hopfield layers as powerful alternatives to traditional attention mechanisms, with superior post-quantization performance. Theoretically, the Outlier-Efficient Modern Hopfield Model retains and improves the desirable properties of standard modern Hopfield models, including fixed point convergence and exponential storage capacity. Empirically, we demonstrate the efficacy of the proposed model across large-scale transformer-based and Hopfield-based models (including BERT, OPT, ViT, and STanHop-Net), benchmarking against state-of-the-art methods like Clipped_Softmax and Gated_Attention. Notably, OutEffHop achieves an average reduction of 22+% in average kurtosis and 26+% in the maximum infinity norm of model outputs across four models. Code is available at GitHub; future updates are on arXiv.
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https://proceedings.mlr.press/v235/hu24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24b/hu24b.pdf
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https://openreview.net/forum?id=tABvuya05B
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Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning
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https://proceedings.mlr.press/v235/hu24b.html
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Yusong Hu, De Cheng, Dingwen Zhang, Nannan Wang, Tongliang Liu, Xinbo Gao
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https://proceedings.mlr.press/v235/hu24b.html
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ICML 2024
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Continual learning (CL) aims to learn from sequentially arriving tasks without catastrophic forgetting (CF). By partitioning the network into two parts based on the Lottery Ticket Hypothesis—one for holding the knowledge of the old tasks while the other for learning the knowledge of the new task—the recent progress has achieved forget-free CL. Although addressing the CF issue well, such methods would encounter serious under-fitting in long-term CL, in which the learning process will continue for a long time and the number of new tasks involved will be much higher. To solve this problem, this paper partitions the network into three parts—with a new part for exploring the knowledge sharing between the old and new tasks. With the shared knowledge, this part of network can be learnt to simultaneously consolidate the old tasks and fit to the new task. To achieve this goal, we propose a task-aware Orthogonal Sparse Network (OSN), which contains shared knowledge induced network partition and sharpness-aware orthogonal sparse network learning. The former partitions the network to select shared parameters, while the latter guides the exploration of shared knowledge through shared parameters. Qualitative and quantitative analyses, show that the proposed OSN induces minimum to no interference with past tasks, i.e., approximately no forgetting, while greatly improves the model plasticity and capacity, and finally achieves the state-of-the-art performances.
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https://proceedings.mlr.press/v235/hu24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24c/hu24c.pdf
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https://openreview.net/forum?id=ojtddicekd
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Q-value Regularized Transformer for Offline Reinforcement Learning
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https://proceedings.mlr.press/v235/hu24c.html
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Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao
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https://proceedings.mlr.press/v235/hu24c.html
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ICML 2024
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Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target returns for each state. However, these methods often struggle with stitching together optimal trajectories from sub-optimal ones due to the inconsistency between the sampled returns within individual trajectories and the optimal returns across multiple trajectories. Fortunately, Dynamic Programming (DP) methods offer a solution by leveraging a value function to approximate optimal future returns for each state, while these techniques are prone to unstable learning behaviors, particularly in long-horizon and sparse-reward scenarios. Building upon these insights, we propose the Q-value regularized Transformer (QT), which combines the trajectory modeling ability of the Transformer with the predictability of optimal future returns from DP methods. QT learns an action-value function and integrates a term maximizing action-values into the training loss of CSM, which aims to seek optimal actions that align closely with the behavior policy. Empirical evaluations on D4RL benchmark datasets demonstrate the superiority of QT over traditional DP and CSM methods, highlighting the potential of QT to enhance the state-of-the-art in offline RL.
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https://proceedings.mlr.press/v235/hu24d.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24d/hu24d.pdf
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https://openreview.net/forum?id=2Asakozn3Z
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HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
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https://proceedings.mlr.press/v235/hu24d.html
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Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao
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https://proceedings.mlr.press/v235/hu24d.html
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ICML 2024
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The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Recent advancements approach this through sequence modeling, leveraging the Transformer architecture’s scalability and the benefits of parameter sharing to exploit task similarities. However, variations in task content and complexity pose significant challenges in policy formulation, necessitating judicious parameter sharing and management of conflicting gradients for optimal policy performance. In this work, we introduce the Harmony Multi-Task Decision Transformer (HarmoDT), a novel solution designed to identify an optimal harmony subspace of parameters for each task. We approach this as a bi-level optimization problem, employing a meta-learning framework that leverages gradient-based techniques. The upper level of this framework is dedicated to learning a task-specific mask that delineates the harmony subspace, while the inner level focuses on updating parameters to enhance the overall performance of the unified policy. Empirical evaluations on a series of benchmarks demonstrate the superiority of HarmoDT, verifying the effectiveness of our approach.
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https://proceedings.mlr.press/v235/hu24e.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24e/hu24e.pdf
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https://openreview.net/forum?id=Sra298VMFM
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An Information Theoretic Approach to Interaction-Grounded Learning
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https://proceedings.mlr.press/v235/hu24e.html
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Xiaoyan Hu, Farzan Farnia, Ho-Fung Leung
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https://proceedings.mlr.press/v235/hu24e.html
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ICML 2024
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Reinforcement learning (RL) problems where the learner attempts to infer an unobserved reward from some feedback variables have been studied in several recent papers. The setting of Interaction-Grounded Learning (IGL) is an example of such feedback-based reinforcement learning tasks where the learner optimizes the return by inferring latent binary rewards from the interaction with the environment. In the IGL setting, a relevant assumption used in the RL literature is that the feedback variable $Y$ is conditionally independent of the context-action $(X,A)$ given the latent reward $R$. In this work, we propose Variational Information-based IGL (VI-IGL) as an information-theoretic method to enforce the conditional independence assumption in the IGL-based RL problem. The VI-IGL framework learns a reward decoder using an information-based objective based on the conditional mutual information (MI) between the context-action $(X,A)$ and the feedback variable $Y$ observed from the environment. To estimate and optimize the information-based terms for the continuous random variables in the RL problem, VI-IGL leverages the variational representation of mutual information and results in a min-max optimization problem. Theoretical analysis shows that the optimization problem can be sample-efficiently solved. Furthermore, we extend the VI-IGL framework to general $f$-Information measures in the information theory literature, leading to the generalized $f$-VI-IGL framework to address the RL problem under the IGL condition. Finally, the empirical results on several reinforcement learning settings indicate an improved performance in comparison to the previous IGL-based RL algorithm.
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https://proceedings.mlr.press/v235/hu24f.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24f/hu24f.pdf
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https://openreview.net/forum?id=stMhi1Sn2G
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Accelerated Speculative Sampling Based on Tree Monte Carlo
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https://proceedings.mlr.press/v235/hu24f.html
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Zhengmian Hu, Heng Huang
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https://proceedings.mlr.press/v235/hu24f.html
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ICML 2024
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Speculative Sampling (SpS) has been introduced to speed up inference of large language models (LLMs) by generating multiple tokens in a single forward pass under the guidance of a reference model, while preserving the original distribution. We observe that SpS can be derived through maximum coupling on the token distribution. However, we find that this approach is not optimal as it applies maximum coupling incrementally for each new token, rather than seeking a global maximum coupling that yields a faster algorithm, given the tree-space nature of LLM generative distributions. In this paper, we shift our focus from distributions on a token space to those on a tree space. We propose a novel class of Tree Monte Carlo (TMC) methods, demonstrating their unbiasedness and convergence. As a particular instance of TMC, our new algorithm, Accelerated Speculative Sampling (ASpS), outperforms traditional SpS by generating more tokens per step on average, achieving faster inference, while maintaining the original distribution.
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https://proceedings.mlr.press/v235/hu24g.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24g/hu24g.pdf
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https://openreview.net/forum?id=gAyzjHw2ml
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SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code
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https://proceedings.mlr.press/v235/hu24g.html
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Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A Ross, Cordelia Schmid, Alireza Fathi
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https://proceedings.mlr.press/v235/hu24g.html
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ICML 2024
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This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial planning and arrangement. We tackle these challenges through a combination of advanced abstraction, strategic planning, and library learning. SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene. SceneCraft then writes Python scripts based on this graph, translating relationships into numerical constraints for asset layout. Next, SceneCraft leverages the perceptual strengths of vision-language foundation models like GPT-V to analyze rendered images and iteratively refine the scene. On top of this process, SceneCraft features a library learning mechanism that compiles common script functions into a reusable library, facilitating continuous self-improvement without expensive LLM parameter tuning. Our evaluation demonstrates that SceneCraft surpasses existing LLM-based agents in rendering complex scenes, as shown by its adherence to constraints and favorable human assessments. We also showcase the broader application potential of SceneCraft by reconstructing detailed 3D scenes from the Sintel movie and guiding a video generative model with generated scenes as intermediary control signal.
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https://proceedings.mlr.press/v235/hu24h.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24h/hu24h.pdf
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https://openreview.net/forum?id=40hCy8n5XH
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InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization
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https://proceedings.mlr.press/v235/hu24h.html
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Zhengyang Hu, Song Kang, Qunsong Zeng, Kaibin Huang, Yanchao Yang
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https://proceedings.mlr.press/v235/hu24h.html
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ICML 2024
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Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. We will make the code and models available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation.
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https://proceedings.mlr.press/v235/hu24i.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24i/hu24i.pdf
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https://openreview.net/forum?id=XnsI1HKAKC
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Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
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https://proceedings.mlr.press/v235/hu24i.html
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Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo
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https://proceedings.mlr.press/v235/hu24i.html
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ICML 2024
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Unsupervised domain adaptation (UDA) has seen substantial efforts to improve model accuracy for an unlabeled target domain with the help of a labeled source domain. However, UDA models often exhibit poorly calibrated predictive uncertainty on target data, a problem that remains under-explored and poses risks in safety-critical UDA applications. The calibration problem in UDA is particularly challenging due to the absence of labeled target data and severe distribution shifts between domains. In this paper, we approach UDA calibration as a target-domain-specific unsupervised problem, different from mainstream solutions based on covariate shift. We introduce Pseudo-Calibration (PseudoCal), a novel post-hoc calibration framework. Our innovative use of inference-stage mixup synthesizes a labeled pseudo-target set capturing the structure of the real unlabeled target data. This turns the unsupervised calibration problem into a supervised one, easily solvable with temperature scaling. Extensive empirical evaluations across 5 diverse UDA scenarios involving 10 UDA methods consistently demonstrate the superior performance and versatility of PseudoCal over existing solutions.
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https://proceedings.mlr.press/v235/hu24j.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24j/hu24j.pdf
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https://openreview.net/forum?id=vXUqOCsbj8
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On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
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https://proceedings.mlr.press/v235/hu24j.html
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Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu
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https://proceedings.mlr.press/v235/hu24j.html
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ICML 2024
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We investigate the computational limits of the memory retrieval dynamics of modern Hopfield models from the fine-grained complexity analysis. Our key contribution is the characterization of a phase transition behavior in the efficiency of all possible modern Hopfield models based on the norm of patterns. Specifically, we establish an upper bound criterion for the norm of input query patterns and memory patterns. Only below this criterion, sub-quadratic (efficient) variants of the modern Hopfield model exist, assuming the Strong Exponential Time Hypothesis (SETH). To showcase our theory, we provide a formal example of efficient constructions of modern Hopfield models using low-rank approximation when the efficient criterion holds. This includes a derivation of a lower bound on the computational time, scaling linearly with $\max$$\{$ # of stored memory patterns, length of input query sequence$\}$. In addition, we prove its memory retrieval error bound and exponential memory capacity.
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https://proceedings.mlr.press/v235/hu24k.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24k/hu24k.pdf
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https://openreview.net/forum?id=YdwwWRX20q
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Improving Interpretation Faithfulness for Vision Transformers
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https://proceedings.mlr.press/v235/hu24k.html
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Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
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https://proceedings.mlr.press/v235/hu24k.html
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ICML 2024
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Vision Transformers (ViTs) have achieved state-of-the-art performance for various vision tasks. One reason behind the success lies in their ability to provide plausible innate explanations for the behavior of neural architectures. However, ViTs suffer from issues with explanation faithfulness, as their focal points are fragile to adversarial attacks and can be easily changed with even slight perturbations on the input image. In this paper, we propose a rigorous approach to mitigate these issues by introducing Faithful ViTs (FViTs). Briefly speaking, an FViT should have the following two properties: (1) The top-$k$ indices of its self-attention vector should remain mostly unchanged under input perturbation, indicating stable explanations; (2) The prediction distribution should be robust to perturbations. To achieve this, we propose a new method called Denoised Diffusion Smoothing (DDS), which adopts randomized smoothing and diffusion-based denoising. We theoretically prove that processing ViTs directly with DDS can turn them into FViTs. We also show that Gaussian noise is nearly optimal for both $\ell_2$ and $\ell_\infty$-norm cases. Finally, we demonstrate the effectiveness of our approach through comprehensive experiments and evaluations. Results show that FViTs are more robust against adversarial attacks while maintaining the explainability of attention, indicating higher faithfulness.
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https://proceedings.mlr.press/v235/hu24l.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24l/hu24l.pdf
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https://openreview.net/forum?id=Nue7KgVZ6e
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Multigroup Robustness
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https://proceedings.mlr.press/v235/hu24l.html
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Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
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https://proceedings.mlr.press/v235/hu24l.html
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ICML 2024
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To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical processes of gathering data may lead to patterns of data corruption that are localized to specific partitions of the training dataset. Motivated by critical applications where the learned model is deployed to make predictions about people from a rich collection of overlapping subpopulations, we initiate the study of multigroup robust algorithms whose robustness guarantees for each subpopulation only degrade with the amount of data corruption inside that subpopulation. When the data corruption is not distributed uniformly over subpopulations, our algorithms provide more meaningful robustness guarantees than standard guarantees that are oblivious to how the data corruption and the affected subpopulations are related. Our techniques establish a new connection between multigroup fairness and robustness.
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https://proceedings.mlr.press/v235/hu24m.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24m/hu24m.pdf
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https://openreview.net/forum?id=IzqpUC34Jg
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Provable Privacy with Non-Private Pre-Processing
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https://proceedings.mlr.press/v235/hu24m.html
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Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf
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https://proceedings.mlr.press/v235/hu24m.html
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ICML 2024
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When analyzing Differentially Private (DP) machine learning pipelines, the potential privacy cost of data-dependent pre-processing is frequently overlooked in privacy accounting. In this work, we propose a general framework to evaluate the additional privacy cost incurred by non-private data-dependent pre-processing algorithms. Our framework establishes upper bounds on the overall privacy guarantees by utilising two new technical notions: a variant of DP termed Smooth DP and the bounded sensitivity of the pre-processing algorithms. In addition to the generic framework, we provide explicit overall privacy guarantees for multiple data-dependent pre-processing algorithms, such as data imputation, quantization, deduplication, standard scaling and PCA, when used in combination with several DP algorithms. Notably, this framework is also simple to implement, allowing direct integration into existing DP pipelines.
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https://proceedings.mlr.press/v235/hu24n.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24n/hu24n.pdf
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https://openreview.net/forum?id=4Vqr8SRfyX
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Case-Based or Rule-Based: How Do Transformers Do the Math?
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https://proceedings.mlr.press/v235/hu24n.html
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Yi Hu, Xiaojuan Tang, Haotong Yang, Muhan Zhang
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https://proceedings.mlr.press/v235/hu24n.html
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ICML 2024
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Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuitive for humans, such as addition. While we can easily learn basic rules of addition and apply them to new problems of any length, LLMs struggle to do the same. Instead, they may rely on similar cases seen in the training corpus for help. We define these two different reasoning mechanisms as "rule-based reasoning" and "case-based reasoning". Since rule-based reasoning is essential for acquiring systematic generalization ability, we aim to explore exactly whether transformers use rule-based or case-based reasoning for math problems. Through carefully designed intervention experiments on five math tasks, we confirm that transformers are performing case-based reasoning, no matter whether scratchpad is used, which aligns with the previous observations that transformers use subgraph matching/shortcut learning to reason. To mitigate such problems, we propose a Rule-Following Fine-Tuning (RFFT) technique to teach transformers to perform rule-based reasoning. Specifically, we provide explicit rules in the input and then instruct transformers to recite and follow the rules step by step. Through RFFT, we successfully enable LLMs fine-tuned on 1-5 digit addition to generalize to up to 12-digit addition with over 95% accuracy, which is over 40% higher than scratchpad. The significant improvement demonstrates that teaching LLMs to use rules explicitly helps them learn rule-based reasoning and generalize better in length. Code is available at https://github.com/GraphPKU/Case_or_Rule.
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https://proceedings.mlr.press/v235/hu24o.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24o/hu24o.pdf
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https://openreview.net/forum?id=T0lFfO8HaK
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Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
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https://proceedings.mlr.press/v235/hu24o.html
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Zixuan Hu, Yongxian Wei, Li Shen, Zhenyi Wang, Lei Li, Chun Yuan, Dacheng Tao
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https://proceedings.mlr.press/v235/hu24o.html
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ICML 2024
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Model inversion, which aims to reconstruct the original training data from pre-trained discriminative models, is especially useful when the original training data is unavailable due to privacy, usage rights, or size constraints. However, existing dense inversion methods attempt to reconstruct the entire image area, making them extremely inefficient when inverting high-resolution images from large-scale Vision Transformers (ViTs). We further identify two underlying causes of this inefficiency: the redundant inversion of noisy backgrounds and the unintended inversion of spurious correlations—a phenomenon we term “hallucination” in model inversion. To address these limitations, we propose a novel sparse model inversion strategy, as a plug-and-play extension to speed up existing dense inversion methods with no need for modifying their original loss functions. Specifically, we selectively invert semantic foregrounds while stopping the inversion of noisy backgrounds and potential spurious correlations. Through both theoretical and empirical studies, we validate the efficacy of our approach in achieving significant inversion acceleration (up to $\times$3.79) while maintaining comparable or even enhanced downstream performance in data-free model quantization and data-free knowledge transfer. Code is available at https://github.com/Egg-Hu/SMI.
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https://proceedings.mlr.press/v235/hu24p.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24p/hu24p.pdf
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https://openreview.net/forum?id=HLHQxMydFk
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Bayesian Design Principles for Offline-to-Online Reinforcement Learning
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https://proceedings.mlr.press/v235/hu24p.html
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Hao Hu, Yiqin Yang, Jianing Ye, Chengjie Wu, Ziqing Mai, Yujing Hu, Tangjie Lv, Changjie Fan, Qianchuan Zhao, Chongjie Zhang
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https://proceedings.mlr.press/v235/hu24p.html
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ICML 2024
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Offline reinforcement learning (RL) is crucial for real-world applications where exploration can be costly or unsafe. However, offline learned policies are often suboptimal, and further online fine-tuning is required. In this paper, we tackle the fundamental dilemma of offline-to-online fine-tuning: if the agent remains pessimistic, it may fail to learn a better policy, while if it becomes optimistic directly, performance may suffer from a sudden drop. We show that Bayesian design principles are crucial in solving such a dilemma. Instead of adopting optimistic or pessimistic policies, the agent should act in a way that matches its belief in optimal policies. Such a probability-matching agent can avoid a sudden performance drop while still being guaranteed to find the optimal policy. Based on our theoretical findings, we introduce a novel algorithm that outperforms existing methods on various benchmarks, demonstrating the efficacy of our approach. Overall, the proposed approach provides a new perspective on offline-to-online RL that has the potential to enable more effective learning from offline data.
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https://proceedings.mlr.press/v235/hu24q.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24q/hu24q.pdf
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https://openreview.net/forum?id=s4h6nyjM9H
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High-Performance Temporal Reversible Spiking Neural Networks with $\mathcalO(L)$ Training Memory and $\mathcalO(1)$ Inference Cost
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https://proceedings.mlr.press/v235/hu24q.html
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Jiakui Hu, Man Yao, Xuerui Qiu, Yuhong Chou, Yuxuan Cai, Ning Qiao, Yonghong Tian, Bo Xu, Guoqi Li
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https://proceedings.mlr.press/v235/hu24q.html
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ICML 2024
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Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This work proposes a novel Temporal Reversible architecture for SNNs (T-RevSNN) to jointly address the training and inference challenges by altering the forward propagation of SNNs. We turn off the temporal dynamics of most spiking neurons and design multi-level temporal reversible interactions at temporal turn-on spiking neurons, resulting in a $\mathcal{O}(L)$ training memory. Combined with the temporal reversible nature, we redesign the input encoding and network organization of SNNs to achieve $\mathcal{O}(1)$ inference energy cost. Then, we finely adjust the internal units and residual connections of the basic SNN block to ensure the effectiveness of sparse temporal information interaction. T-RevSNN achieves excellent accuracy on ImageNet, while the memory efficiency, training time acceleration and inference energy efficiency can be significantly improved by $8.6 \times$, $2.0 \times$ and $1.6 \times$, respectively. This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining both high performance and low inference energy cost.
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https://proceedings.mlr.press/v235/hu24r.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24r/hu24r.pdf
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https://openreview.net/forum?id=kTaX87Zn6M
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Accelerating Transformer Pre-training with 2:4 Sparsity
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https://proceedings.mlr.press/v235/hu24r.html
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Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen, Jun Zhu
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https://proceedings.mlr.press/v235/hu24r.html
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ICML 2024
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Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of accelerating feed-forward networks (FFNs) of transformers in pre-training. First, we define a “flip rate” to monitor the stability of a 2:4 training process. Utilizing this metric, we propose three techniques to preserve accuracy: to modify the sparse-refined straight-through estimator by applying the masked decay term on gradients, to determine a feasible decay factor in warm-up stage, and to enhance the model’s quality by a dense fine-tuning procedure near the end of pre-training. Besides, we devise two techniques to practically accelerate training: to calculate transposable 2:4 masks by convolution, and to accelerate gated activation functions by reducing GPU L2 cache miss. Experiments show that our 2:4 sparse training algorithm achieves similar convergence to dense training algorithms on several transformer pre-training tasks, while actual acceleration can be observed on different shapes of transformer block apparently. Our toolkit is available at https://github.com/huyz2023/2by4-pretrain.
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https://proceedings.mlr.press/v235/hu24s.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24s/hu24s.pdf
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https://openreview.net/forum?id=d5LURMSfTx
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InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
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https://proceedings.mlr.press/v235/hu24s.html
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Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu
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https://proceedings.mlr.press/v235/hu24s.html
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ICML 2024
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In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. Agents need to solve these tasks end-to-end by interacting with an execution environment. This benchmark contains DAEval, a dataset consisting of 603 data analysis questions derived from 124 CSV files, and an agent framework which incorporates LLMs to serve as data analysis agents for both serving and evaluating. Since data analysis questions are often open-ended and hard to evaluate without human supervision, we adopt a format-prompting technique to convert each question into a closed-form format so that they can be automatically evaluated. Our extensive benchmarking of 34 LLMs uncovers the current challenges encountered in data analysis tasks. In addition, building upon our agent framework, we develop a specialized agent, DAAgent, which surpasses GPT-3.5 by 3.9% on DABench. Evaluation datasets and toolkits for InfiAgent-DABench are released at https://github.com/InfiAgent/InfiAgent.
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https://proceedings.mlr.press/v235/hua24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/hua24a/hua24a.pdf
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https://openreview.net/forum?id=93gjGDwqim
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ReconBoost: Boosting Can Achieve Modality Reconcilement
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https://proceedings.mlr.press/v235/hua24a.html
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Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, Qingming Huang
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https://proceedings.mlr.press/v235/hua24a.html
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ICML 2024
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This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman’s Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the proposed method.
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https://proceedings.mlr.press/v235/huang24a.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24a/huang24a.pdf
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https://openreview.net/forum?id=eZiQWM5U0E
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Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
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https://proceedings.mlr.press/v235/huang24a.html
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Feihu Huang
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https://proceedings.mlr.press/v235/huang24a.html
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ICML 2024
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Bilevel optimization is widely applied in many machine learning tasks such as hyper-parameter learning, meta learning and reinforcement learning. Although many algorithms recently have been developed to solve the bilevel optimization problems, they generally rely on the (strongly) convex lower-level problems. More recently, some methods have been proposed to solve the nonconvex-PL bilevel optimization problems, where their upper-level problems are possibly nonconvex, and their lower-level problems are also possibly nonconvex while satisfying Polyak-Łojasiewicz (PL) condition. However, these methods still have a high convergence complexity or a high computation complexity such as requiring compute expensive Hessian/Jacobian matrices and its inverses. In the paper, thus, we propose an efficient Hessian/Jacobian-free method (i.e., HJFBiO) with the optimal convergence complexity to solve the nonconvex-PL bilevel problems. Theoretically, under some mild conditions, we prove that our HJFBiO method obtains an optimal convergence rate of $O(\frac{1}{T})$, where $T$ denotes the number of iterations, and has an optimal gradient complexity of $O(\epsilon^{-1})$ in finding an $\epsilon$-stationary solution. We conduct some numerical experiments on the bilevel PL game and hyper-representation learning task to demonstrate efficiency of our proposed method.
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https://proceedings.mlr.press/v235/huang24b.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24b/huang24b.pdf
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https://openreview.net/forum?id=7ckuC9C2FZ
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NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors
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https://proceedings.mlr.press/v235/huang24b.html
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Shi-Sheng Huang, Guo Chen, Chen Li Heng, Hua Huang
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https://proceedings.mlr.press/v235/huang24b.html
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ICML 2024
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The neural implicit surface reconstruction from unorganized points is still challenging, especially when the point clouds are incomplete and/or noisy with complex topology structure. Unlike previous approaches performing neural implicit surface learning relying on local shape priors, this paper proposes to utilize global shape priors to regularize the neural implicit function learning for more reliable surface reconstruction. To this end, we first introduce a differentiable module to generate a smooth indicator function, which globally encodes both the indicative prior and local SDFs of the entire input point cloud. Benefit from this, we propose a new framework, called NeuralIndicator, to jointly learn both the smooth indicator function and neural implicit function simultaneously, using the global shape prior encoded by smooth indicator function to effectively regularize the neural implicit function learning, towards reliable and high-fidelity surface reconstruction from unorganized points without any normal information. Extensive evaluations on synthetic and real-scan datasets show that our approach consistently outperforms previous approaches, especially when point clouds are incomplete and/or noisy with complex topology structure.
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https://proceedings.mlr.press/v235/huang24c.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24c/huang24c.pdf
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https://openreview.net/forum?id=VnI9200eeL
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Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games
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https://proceedings.mlr.press/v235/huang24c.html
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Kexin Huang, Ziqian Chen, Xue Wang, Chongming Gao, Jinyang Gao, Bolin Ding, Xiang Wang
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https://proceedings.mlr.press/v235/huang24c.html
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ICML 2024
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Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetries, poses significant challenges in efficiently solving for equilibria. Traditional learning approaches often face limitations due to their specificity to certain settings and high resource demands. Addressing this, we introduce Auctionformer, an efficient transformer-based method to solve equilibria of diverse auctions in a unified framework. Leveraging the flexible tokenization schemes, Auctionformer translates varying auction games into a standard token series, making use of renowned Transformer architectures. Moreover, we employ Nash error as the loss term, sidestepping the need for underlying equilibrium solutions and enabling efficient training and inference. Furthermore, a few-shot framework supports adaptability to new mechanisms, reinforced by a self-supervised fine-tuning approach. Extensive experimental results affirm the superior performance of Auctionformer over contemporary methods, heralding its potential for broad real-world applications.
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https://proceedings.mlr.press/v235/huang24d.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24d/huang24d.pdf
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https://openreview.net/forum?id=9GLvXGkUE2
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In-context Convergence of Transformers
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https://proceedings.mlr.press/v235/huang24d.html
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Yu Huang, Yuan Cheng, Yingbin Liang
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https://proceedings.mlr.press/v235/huang24d.html
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ICML 2024
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Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can solve an unseen task by utilizing task-specific prompts without further parameters fine-tuning. This also inspired recent theoretical studies aiming to understand the in-context learning mechanism of transformers, which however focused only on linear transformers. In this work, we take the first step toward studying the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent in order to in-context learn linear function classes. We consider a structured data model, where each token is randomly sampled from a set of feature vectors in either balanced or imbalanced fashion. For data with balanced features, we establish the finite-time convergence guarantee with near-zero prediction error by navigating our analysis over two phases of the training dynamics of the attention map. More notably, for data with imbalanced features, we show that the learning dynamics take a stage-wise convergence process, where the transformer first converges to a near-zero prediction error for the query tokens of dominant features, and then converges later to a near-zero error for query tokens of under-represented features, via one and four training phases. Our proof features new techniques for analyzing the competing strengths of two types of attention weights, the change of which determines different training phases.
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https://proceedings.mlr.press/v235/huang24e.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24e/huang24e.pdf
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https://openreview.net/forum?id=EHjm3sXPFy
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Near-Linear Time Approximation Algorithms for k-means with Outliers
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https://proceedings.mlr.press/v235/huang24e.html
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Junyu Huang, Qilong Feng, Ziyun Huang, Jinhui Xu, Jianxin Wang
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https://proceedings.mlr.press/v235/huang24e.html
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ICML 2024
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The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio Δ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time $O(\frac{ndk\log\log n}{\epsilon^2})$ with O(k/ϵ) centers opened and (1+ϵ)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/ϵ)-approximate solution can be obtained in time $O(\frac{ndk\log \log n}{\epsilon^2} + dpoly(k, \frac{1}{\epsilon})\log(n\Delta))$ with (1+ϵ)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem.
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https://proceedings.mlr.press/v235/huang24f.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24f/huang24f.pdf
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https://openreview.net/forum?id=zatLnLvbs8
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Contrastive Predict-and-Search for Mixed Integer Linear Programs
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https://proceedings.mlr.press/v235/huang24f.html
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Taoan Huang, Aaron M Ferber, Arman Zharmagambetov, Yuandong Tian, Bistra Dilkina
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https://proceedings.mlr.press/v235/huang24f.html
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ICML 2024
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Mixed integer linear programs (MILP) are flexible and powerful tools for modeling and solving many difficult real-world combinatorial optimization problems. In this paper, we propose a novel machine learning (ML)-based framework ConPaS that learns to predict solutions to MILPs with contrastive learning. For training, we collect high-quality solutions as positive samples. We also collect low-quality or infeasible solutions as negative samples using novel optimization-based or sampling approaches. We then learn to make discriminative predictions by contrasting the positive and negative samples. During testing, we predict and fix the assignments for a subset of integer variables and then solve the resulting reduced MILP to find high-quality solutions. Empirically, ConPaS achieves state-of-the-art results compared to other ML-based approaches in terms of the quality of and the speed at which solutions are found.
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https://proceedings.mlr.press/v235/huang24g.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24g/huang24g.pdf
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https://openreview.net/forum?id=g8AigOTNXL
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Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
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https://proceedings.mlr.press/v235/huang24g.html
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Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli Shama Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
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https://proceedings.mlr.press/v235/huang24g.html
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ICML 2024
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We study the problem of symbolic music generation (e.g., generating piano rolls), with a technical focus on non-differentiable rule guidance. Musical rules are often expressed in symbolic form on note characteristics, such as note density or chord progression, many of which are non-differentiable which pose a challenge when using them for guided diffusion. We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time. Additionally, we introduce a latent diffusion architecture for symbolic music generation with high time resolution, which can be composed with SCG in a plug-and-play fashion. Compared to standard strong baselines in symbolic music generation, this framework demonstrates marked advancements in music quality and rule-based controllability, outperforming current state-of-the-art generators in a variety of settings. For detailed demonstrations, code and model checkpoints, please visit our project website.
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https://proceedings.mlr.press/v235/huang24h.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24h/huang24h.pdf
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https://openreview.net/forum?id=vhMq3eAB34
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DiffDA: a Diffusion model for weather-scale Data Assimilation
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https://proceedings.mlr.press/v235/huang24h.html
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Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter Dominik Dueben, Torsten Hoefler
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https://proceedings.mlr.press/v235/huang24h.html
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ICML 2024
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The generation of initial conditions via accurate data assimilation is crucial for weather forecasting and climate modeling. We propose DiffDA as a denoising diffusion model capable of assimilating atmospheric variables using predicted states and sparse observations. Acknowledging the similarity between a weather forecast model and a denoising diffusion model dedicated to weather applications, we adapt the pretrained GraphCast neural network as the backbone of the diffusion model. Through experiments based on simulated observations from the ERA5 reanalysis dataset, our method can produce assimilated global atmospheric data consistent with observations at 0.25$^\circ$ ($\approx$30km) resolution globally. This marks the highest resolution achieved by ML data assimilation models. The experiments also show that the initial conditions assimilated from sparse observations (less than 0.96% of gridded data) and 48-hour forecast can be used for forecast models with a loss of lead time of at most 24 hours compared to initial conditions from state-of-the-art data assimilation in ERA5. This enables the application of the method to real-world applications, such as creating reanalysis datasets with autoregressive data assimilation.
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https://proceedings.mlr.press/v235/huang24i.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24i/huang24i.pdf
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https://openreview.net/forum?id=4ye2I5OelI
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Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
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https://proceedings.mlr.press/v235/huang24i.html
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Jiawei Huang, Niao He, Andreas Krause
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https://proceedings.mlr.press/v235/huang24i.html
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ICML 2024
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We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash Equilibrium policy. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity. Notably, P-MBED measures the complexity of the single-agent model class converted from the given mean-field model class, and potentially, can be exponentially lower than the MBED proposed by Huang et al. (2024). We contribute a model elimination algorithm featuring a novel exploration strategy and establish sample complexity results polynomial w.r.t. P-MBED. Crucially, our results reveal that, under the basic realizability and Lipschitz continuity assumptions, learning Nash Equilibrium in MFGs is no more statistically challenging than solving a logarithmic number of single-agent RL problems. We further extend our results to Multi-Type MFGs, generalizing from conventional MFGs and involving multiple types of agents. This extension implies statistical tractability of a broader class of Markov Games through the efficacy of mean-field approximation. Finally, inspired by our theoretical algorithm, we present a heuristic approach with improved computational efficiency and empirically demonstrate its effectiveness.
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https://proceedings.mlr.press/v235/huang24j.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24j/huang24j.pdf
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https://openreview.net/forum?id=jmmji1EU3g
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In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
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https://proceedings.mlr.press/v235/huang24j.html
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Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang
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https://proceedings.mlr.press/v235/huang24j.html
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ICML 2024
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In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36$\times$ times faster than baselines in the D4RL benchmark and 27$\times$ times faster in the Grid World benchmark.
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https://proceedings.mlr.press/v235/huang24k.html
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https://raw.githubusercontent.com/mlresearch/v235/main/assets/huang24k/huang24k.pdf
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https://openreview.net/forum?id=xlWcdtCyOC
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InstructSpeech: Following Speech Editing Instructions via Large Language Models
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https://proceedings.mlr.press/v235/huang24k.html
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Rongjie Huang, Ruofan Hu, Yongqi Wang, Zehan Wang, Xize Cheng, Ziyue Jiang, Zhenhui Ye, Dongchao Yang, Luping Liu, Peng Gao, Zhou Zhao
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https://proceedings.mlr.press/v235/huang24k.html
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ICML 2024
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Instruction-guided speech editing aims to follow the user’s natural language instruction to manipulate the semantic and acoustic attributes of a speech. In this work, we construct triplet paired data (instruction, input speech, output speech) to alleviate data scarcity and train a multi-task large language model named InstructSpeech. To mitigate the challenges of accurately executing user’s instructions, we 1) introduce the learned task embeddings with a fine-tuned Flan-T5-XL to guide the generation process towards the correct generative task; 2) include an extensive and diverse set of speech editing and processing tasks to enhance model capabilities; 3) investigate chain-of-thought reasoning for free-form semantic content editing; and 4) propose a hierarchical adapter that effectively updates a small portion of parameters for generalization to new tasks. To assess instruction speech editing in greater depth, we introduce a benchmark evaluation with contrastive instruction-speech pre-training (CISP) to test the speech quality and instruction-speech alignment faithfulness. Experimental results demonstrate that InstructSpeech achieves state-of-the-art results in eleven tasks, for the first time unlocking the ability to edit speech’s acoustic and semantic attributes following a user’s instruction. Audio samples are available at https://InstructSpeech.github.io
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