abs
stringlengths 44
64
| Download PDF
stringlengths 75
115
| OpenReview
stringlengths 42
42
| title
stringlengths 15
148
| url
stringlengths 44
64
| authors
stringlengths 6
903
| detail_url
stringlengths 44
64
| tags
stringclasses 1
value | abstract
stringlengths 422
5.84k
|
|---|---|---|---|---|---|---|---|---|
https://proceedings.mlr.press/v235/zang24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zang24a/zang24a.pdf
|
https://openreview.net/forum?id=s0UDX7Kswl
|
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
|
https://proceedings.mlr.press/v235/zang24a.html
|
Zelin Zang, Hao Luo, Kai Wang, Panpan Zhang, Fan Wang, Stan Z. Li, Yang You
|
https://proceedings.mlr.press/v235/zang24a.html
|
ICML 2024
|
Unsupervised Contrastive learning has gained prominence in fields such as vision, and biology, leveraging predefined positive/negative samples for representation learning. Data augmentation, categorized into hand-designed and model-based methods, has been identified as a crucial component for enhancing contrastive learning. However, hand-designed methods require human expertise in domain-specific data while sometimes distorting the meaning of the data. In contrast, generative model-based approaches usually require supervised or large-scale external data, which has become a bottleneck constraining model training in many domains. To address the problems presented above, this paper proposes DiffAug, a novel unsupervised contrastive learning technique with diffusion mode-based positive data generation. DiffAug consists of a semantic encoder and a conditional diffusion model; the conditional diffusion model generates new positive samples conditioned on the semantic encoding to serve the training of unsupervised contrast learning. With the help of iterative training of the semantic encoder and diffusion model, DiffAug improves the representation ability in an uninterrupted and unsupervised manner. Experimental evaluations show that DiffAug outperforms hand-designed and SOTA model-based augmentation methods on DNA sequence, visual, and bio-feature datasets. The code for review is released at DiffAug CODE.
|
https://proceedings.mlr.press/v235/zarifis24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zarifis24a/zarifis24a.pdf
|
https://openreview.net/forum?id=AZ1tWCa9j3
|
Robustly Learning Single-Index Models via Alignment Sharpness
|
https://proceedings.mlr.press/v235/zarifis24a.html
|
Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas
|
https://proceedings.mlr.press/v235/zarifis24a.html
|
ICML 2024
|
We study the problem of learning Single-Index Models under the $L_2^2$ loss in the agnostic model. We give an efficient learning algorithm, achieving a constant factor approximation to the optimal loss, that succeeds under a range of distributions (including log-concave distributions) and a broad class of monotone and Lipschitz link functions. This is the first efficient constant factor approximate agnostic learner, even for Gaussian data and for any nontrivial class of link functions. Prior work for the case of unknown link function either works in the realizable setting or does not attain constant factor approximation. The main technical ingredient enabling our algorithm and analysis is a novel notion of a local error bound in optimization that we term alignment sharpness and that may be of broader interest.
|
https://proceedings.mlr.press/v235/zarifzadeh24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zarifzadeh24a/zarifzadeh24a.pdf
|
https://openreview.net/forum?id=sT7UJh5CTc
|
Low-Cost High-Power Membership Inference Attacks
|
https://proceedings.mlr.press/v235/zarifzadeh24a.html
|
Sajjad Zarifzadeh, Philippe Liu, Reza Shokri
|
https://proceedings.mlr.press/v235/zarifzadeh24a.html
|
ICML 2024
|
Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attacks (RMIA) with low computational overhead. We achieve this by a fine-grained modeling of the null hypothesis in our likelihood ratio tests, and effectively leveraging both reference models and reference population data samples. RMIA has superior test power compared with prior methods, throughout the TPR-FPR curve (even at extremely low FPR, as low as 0). Under computational constraints, where only a limited number of pre-trained reference models (as few as 1) are available, and also when we vary other elements of the attack (e.g., data distribution), our method performs exceptionally well, unlike prior attacks that approach random guessing. RMIA lays the groundwork for practical yet accurate data privacy risk assessment in machine learning.
|
https://proceedings.mlr.press/v235/zeighami24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeighami24a/zeighami24a.pdf
|
https://openreview.net/forum?id=oowQ8LPA12
|
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
|
https://proceedings.mlr.press/v235/zeighami24a.html
|
Sepanta Zeighami, Cyrus Shahabi
|
https://proceedings.mlr.press/v235/zeighami24a.html
|
ICML 2024
|
Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also show performance degradation for learned models, possibly to worse than non-learned alternatives. This, together with a lack of theoretical understanding of learned methods undermines their practical applicability, since there are no guarantees on how well the models will perform after deployment. In this paper, we present the first known theoretical characterization of the performance of learned models in dynamic datasets, for the aforementioned operations. Our results show novel theoretical characteristics achievable by learned models and provide bounds on the performance of the models that characterize their advantages over non-learned methods, showing why and when learned models can outperform the alternatives. Our analysis develops the distribution learnability framework and novel theoretical tools which build the foundation for the analysis of learned database operations in the future.
|
https://proceedings.mlr.press/v235/zeng24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24a/zeng24a.pdf
|
https://openreview.net/forum?id=cZNuYKtoOZ
|
Continuous Treatment Effects with Surrogate Outcomes
|
https://proceedings.mlr.press/v235/zeng24a.html
|
Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan A. Rossi, Ritwik Sinha, Edward Kennedy
|
https://proceedings.mlr.press/v235/zeng24a.html
|
ICML 2024
|
In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missingness depends on covariates (i.e., missingness is not completely at random), analyses based on fully observed samples alone may be biased. Incorporating surrogates, which are fully observed post-treatment variables related to the primary outcome, can improve estimation in this case. In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem. Importantly, we establish the asymptotic normality of the proposed estimator and show possible improvements on the variance compared with methods that solely use labeled data. Extensive simulations show our methods enjoy appealing empirical performance.
|
https://proceedings.mlr.press/v235/zeng24b.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24b/zeng24b.pdf
|
https://openreview.net/forum?id=LVgT0ShxN5
|
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
|
https://proceedings.mlr.press/v235/zeng24b.html
|
Junhua Zeng, Chao Li, Zhun Sun, Qibin Zhao, Guoxu Zhou
|
https://proceedings.mlr.press/v235/zeng24b.html
|
ICML 2024
|
Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem. Although several works have targeted TN-SS, most existing algorithms are manually crafted heuristics with poor performance, suffering from the curse of dimensionality and local convergence. In this work, we jump out of the box, studying how to harness large language models (LLMs) to automatically discover new TN-SS algorithms, replacing the involvement of human experts. By observing how human experts innovate in research, we model their common workflow and propose an automatic algorithm discovery framework called tnGPS. The proposed framework is an elaborate prompting pipeline that instruct LLMs to generate new TN-SS algorithms through iterative refinement and enhancement. The experimental results demonstrate that the algorithms discovered by tnGPS exhibit superior performance in benchmarks compared to the current state-of-the-art methods. Our code is available at https://github.com/ChaoLiAtRIKEN/tngps.
|
https://proceedings.mlr.press/v235/zeng24c.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24c/zeng24c.pdf
|
https://openreview.net/forum?id=1RZKuvqYCR
|
Token-level Direct Preference Optimization
|
https://proceedings.mlr.press/v235/zeng24c.html
|
Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, Jun Wang
|
https://proceedings.mlr.press/v235/zeng24c.html
|
ICML 2024
|
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often utilizes methods like pairwise comparisons and KL divergence against a reference LLM, focusing on the evaluation of full answers generated by the models. However, the generation of these responses occurs in a token level, following a sequential, auto-regressive fashion. In this paper, we introduce Token-level Direct Preference Optimization (TDPO), a novel approach to align LLMs with human preferences by optimizing policy at the token level. Unlike previous methods, which face challenges in divergence efficiency, TDPO integrates forward KL divergence constraints for each token, improving alignment and diversity. Utilizing the Bradley-Terry model for a token-based reward system, our method enhances the regulation of KL divergence, while preserving simplicity without the need for explicit reward modeling. Experimental results across various text tasks demonstrate TDPO’s superior performance in balancing alignment with generation diversity. Notably, fine-tuning with TDPO strikes a better balance than DPO in the controlled sentiment generation and single-turn dialogue datasets, and significantly improves the quality of generated responses compared to both DPO and PPO-based RLHF methods.
|
https://proceedings.mlr.press/v235/zeng24d.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24d/zeng24d.pdf
|
https://openreview.net/forum?id=Z19JQ6WFtJ
|
Learning Reward for Robot Skills Using Large Language Models via Self-Alignment
|
https://proceedings.mlr.press/v235/zeng24d.html
|
Yuwei Zeng, Yao Mu, Lin Shao
|
https://proceedings.mlr.press/v235/zeng24d.html
|
ICML 2024
|
Learning reward functions remains the bottleneck to equip a robot with a broad repertoire of skills. Large Language Models (LLM) contain valuable task-related knowledge that can potentially aid in the learning of reward functions. However, the proposed reward function can be imprecise, thus ineffective which requires to be further grounded with environment information. We proposed a method to learn rewards more efficiently in the absence of humans. Our approach consists of two components: We first use the LLM to propose features and parameterization of the reward, then update the parameters through an iterative self-alignment process. In particular, the process minimizes the ranking inconsistency between the LLM and the learnt reward functions based on the execution feedback. The method was validated on 9 tasks across 2 simulation environments. It demonstrates a consistent improvement in training efficacy and efficiency, meanwhile consuming significantly fewer GPT tokens compared to the alternative mutation-based method.
|
https://proceedings.mlr.press/v235/zeng24e.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24e/zeng24e.pdf
|
https://openreview.net/forum?id=PKdege0U6Z
|
Graph Mixup on Approximate Gromov–Wasserstein Geodesics
|
https://proceedings.mlr.press/v235/zeng24e.html
|
Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, Hanghang Tong
|
https://proceedings.mlr.press/v235/zeng24e.html
|
ICML 2024
|
Mixup, which generates synthetic training samples on the data manifold, has been shown to be highly effective in augmenting Euclidean data. However, finding a proper data manifold for graph data is non-trivial, as graphs are non-Euclidean data in disparate spaces. Though efforts have been made, most of the existing graph mixup methods neglect the intrinsic geodesic guarantee, thereby generating inconsistent sample-label pairs. To address this issue, we propose GeoMix to mixup graphs on the Gromov-Wasserstein (GW) geodesics. A joint space over input graphs is first defined based on the GW distance, and graphs are then transformed into the GW space through equivalence-preserving transformations. We further show that the linear interpolation of the transformed graph pairs defines a geodesic connecting the original pairs on the GW manifold, hence ensuring the consistency between generated samples and labels. An accelerated mixup algorithm on the approximate low-dimensional GW manifold is further proposed. Extensive experiments show that the proposed GeoMix promotes the generalization and robustness of GNN models.
|
https://proceedings.mlr.press/v235/zeng24f.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24f/zeng24f.pdf
|
https://openreview.net/forum?id=CQH63IbI5o
|
Interacting Diffusion Processes for Event Sequence Forecasting
|
https://proceedings.mlr.press/v235/zeng24f.html
|
Mai Zeng, Florence Regol, Mark Coates
|
https://proceedings.mlr.press/v235/zeng24f.html
|
ICML 2024
|
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.
|
https://proceedings.mlr.press/v235/zeng24g.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24g/zeng24g.pdf
|
https://openreview.net/forum?id=b2D9PBNNQ2
|
IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers
|
https://proceedings.mlr.press/v235/zeng24g.html
|
Zhanpeng Zeng, Karthikeyan Sankaralingam, Vikas Singh
|
https://proceedings.mlr.press/v235/zeng24g.html
|
ICML 2024
|
GEneral Matrix Multiply (GEMM) is a central operation in deep learning and corresponds to a large chunk of the compute footprint. Therefore, improving its efficiency is an active topic of research. A popular strategy is the use of low bit-width integers to approximate the original matrix entries. This allows efficiency gains, but often requires sophisticated techniques to control the rounding error. In this work, we first verify that when the low bit-width restriction is removed, for a variety of Transformer-based models, integers are, in fact, sufficient for all GEMMs need – for both training and inference stages, and achieve parity (with floating point). No sophisticated techniques are needed. We find that while a large majority of entries in matrices (encountered in such models) can be easily represented by low bit-width integers, the existence of a few heavy hitter entries make it difficult to achieve efficiency gains via the exclusive use of low bit-width GEMMs alone. To address this issue, we develop a simple algorithm, Integer Matrix Unpacking (IM-Unpack), to unpack a matrix with large integer entries into a larger matrix whose entries all lie within the representable range of arbitrarily low bit-width integers. This allows equivalence with the original GEMM, i.e., the exact result can be obtained using purely low bit-width integer GEMMs. This comes at the cost of additional operations – we show that for many popular models, this overhead is quite small. Code is available at https://github.com/vsingh-group/im-unpack.
|
https://proceedings.mlr.press/v235/zenn24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zenn24a/zenn24a.pdf
|
https://openreview.net/forum?id=rvaN2P1rvC
|
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution
|
https://proceedings.mlr.press/v235/zenn24a.html
|
Johannes Zenn, Robert Bamler
|
https://proceedings.mlr.press/v235/zenn24a.html
|
ICML 2024
|
Differentiable annealed importance sampling (DAIS), proposed by Geffner & Domke (2021) and Zhang et al. (2021), allows optimizing, among others, over the initial distribution of AIS. In this paper, we show that, in the limit of many transitions, DAIS minimizes the symmetrized KL divergence (Jensen-Shannon divergence) between the initial and target distribution. Thus, DAIS can be seen as a form of variational inference (VI) in that its initial distribution is a parametric fit to an intractable target distribution. We empirically evaluate the usefulness of the initial distribution as a variational distribution on synthetic and real-world data, observing that it often provides more accurate uncertainty estimates than standard VI (optimizing the reverse KL divergence), importance weighted VI, and Markovian score climbing (optimizing the forward KL divergence).
|
https://proceedings.mlr.press/v235/zeynali24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeynali24a/zeynali24a.pdf
|
https://openreview.net/forum?id=XyhgssAo5b
|
Robust Learning-Augmented Dictionaries
|
https://proceedings.mlr.press/v235/zeynali24a.html
|
Ali Zeynali, Shahin Kamali, Mohammad Hajiesmaili
|
https://proceedings.mlr.press/v235/zeynali24a.html
|
ICML 2024
|
We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness. Our data structure, named RobustSL, is a Skip list augmented by predictions of access frequencies of elements in a data sequence. With proper predictions, RobustSL has optimal consistency (achieves static optimality). At the same time, it maintains a logarithmic running time for each operation, ensuring optimal robustness, even if predictions are generated adversarially. Therefore, RobustSL has all the advantages of the recent learning-augmented data structures of Lin, Luo, and Woodruff (ICML 2022) and Cao et al. (arXiv 2023), while providing robustness guarantees that are absent in the previous work. Numerical experiments show that RobustSL outperforms alternative data structures using both synthetic and real datasets.
|
https://proceedings.mlr.press/v235/zhai24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhai24a/zhai24a.pdf
|
https://openreview.net/forum?id=xye7iNsgXn
|
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
|
https://proceedings.mlr.press/v235/zhai24a.html
|
Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Jiayuan He, Yinghai Lu, Yu Shi
|
https://proceedings.mlr.press/v235/zhai24a.html
|
ICML 2024
|
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in industry fail to scale with compute. Inspired by success achieved by Transformers in language and vision domains, we revisit fundamental design choices in recommendation systems. We reformulate recommendation problems as sequential transduction tasks within a generative modeling framework (“Generative Recommenders”), and propose a new architecture, HSTU, designed for high cardinality, non-stationary streaming recommendation data. HSTU outperforms baselines over synthetic and public datasets by up to 65.8% in NDCG, and is 5.3x to 15.2x faster than FlashAttention2-based Transformers on 8192 length sequences. HSTU-based Generative Recommenders, with 1.5 trillion parameters, improve metrics in online A/B tests by 12.4% and have been deployed on multiple surfaces of a large internet platform with billions of users. More importantly, the model quality of Generative Recommenders empirically scales as a power-law of training compute across three orders of magnitude, up to GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model developments, and further paves the way for the first foundation models in recommendations.
|
https://proceedings.mlr.press/v235/zhang24a.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24a/zhang24a.pdf
|
https://openreview.net/forum?id=jEWpcEyuUl
|
Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
|
https://proceedings.mlr.press/v235/zhang24a.html
|
Zhiheng Zhang
|
https://proceedings.mlr.press/v235/zhang24a.html
|
ICML 2024
|
Partial identification (PI) presents a significant challenge in causal inference due to the incomplete measurement of confounders. Given that obtaining auxiliary variables of confounders is not always feasible and relies on untestable assumptions, researchers are encouraged to explore the internal information of latent confounders without external assistance. However, these prevailing PI results often lack precise mathematical measurement from observational data or assume that the information pertaining to confounders falls within extreme scenarios. In our paper, we reassess the significance of the marginal confounder distribution in PI. We refrain from imposing additional restrictions on the marginal confounder distribution, such as entropy or mutual information. Instead, we establish the closed-form tight PI for any possible P(U) in the discrete case. Furthermore, we establish the if and only if criterion for discerning whether the marginal confounder information leads to non-vanilla PI regions. This reveals a fundamental negative result wherein the marginal confounder information minimally contributes to PI as the confounder’s cardinality increases. Our theoretical findings are supported by experiments.
|
https://proceedings.mlr.press/v235/zhang24b.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24b/zhang24b.pdf
|
https://openreview.net/forum?id=0hbeZQm1Se
|
DAG-Based Column Generation for Adversarial Team Games
|
https://proceedings.mlr.press/v235/zhang24b.html
|
Youzhi Zhang, Bo An, Daniel Dajun Zeng
|
https://proceedings.mlr.press/v235/zhang24b.html
|
ICML 2024
|
Many works recently have focused on computing optimal solutions for the ex ante coordination of a team for solving sequential adversarial team games, where a team of players coordinate against an opponent (or a team of players) in a zero-sum extensive-form game. However, it is challenging to directly compute such an optimal solution because the team’s coordinated strategy space is exponential in the size of the game tree due to the asymmetric information of team members. Column Generation (CG) algorithms have been proposed to overcome this challenge by iteratively expanding the team’s coordinated strategy space via a Best Response Oracle (BRO). More recently, more compact representations (particularly, the Team Belief Directed Acyclic Graph (TB-DAG)) of the team’s coordinated strategy space have been proposed, but the TB-DAG-based algorithms only outperform the CG-based algorithms in games with a small TB-DAG. Unfortunately, it is inefficient to directly apply CG to the TB-DAG because the size of the TB-DAG is still exponential in the size of the game tree and then makes the BRO unscalable. To this end, we develop our novel TB-DAG CG (DCG) algorithm framework by computing a coordinated best response in the original game first and then transforming this strategy into the TB-DAG form. To further improve the scalability, we propose a more suitable BRO for DCG to reduce the cost of the transformation at each iteration. We theoretically show that our algorithm converges exponentially faster than the state-of-the-art CG algorithms, and experimental results show that our algorithm is at least two orders of magnitude faster than the state-of-the-art baselines.
|
https://proceedings.mlr.press/v235/zhang24c.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24c/zhang24c.pdf
|
https://openreview.net/forum?id=ryDa4mS18V
|
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
|
https://proceedings.mlr.press/v235/zhang24c.html
|
Junjie Zhang, Chenjia Bai, Haoran He, Zhigang Wang, Bin Zhao, Xiu Li, Xuelong Li
|
https://proceedings.mlr.press/v235/zhang24c.html
|
ICML 2024
|
Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of scene understanding and action prediction. Current methods employ both 3D representation and multi-view 2D representation to predict the poses of the robot’s end-effector. However, they still require a considerable amount of high-quality robot trajectories, and suffer from limited generalization in unseen tasks and inefficient execution in long-horizon reasoning. In this paper, we propose SAM-E, a novel architecture for robot manipulation by leveraging a vision-foundation model for generalizable scene understanding and sequence imitation for long-term action reasoning. Specifically, we adopt Segment Anything (SAM) pre-trained on a huge number of images and promptable masks as the foundation model for extracting task-relevant features, and employ parameter-efficient fine-tuning on robot data for a better understanding of embodied scenarios. To address long-horizon reasoning, we develop a novel multi-channel heatmap that enables the prediction of the action sequence in a single pass, notably enhancing execution efficiency. Experimental results from various instruction-following tasks demonstrate that SAM-E achieves superior performance with higher execution efficiency compared to the baselines, and also significantly improves generalization in few-shot adaptation to new tasks.
|
https://proceedings.mlr.press/v235/zhang24d.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24d/zhang24d.pdf
|
https://openreview.net/forum?id=oTYuORAMaP
|
Efficient Stochastic Approximation of Minimax Excess Risk Optimization
|
https://proceedings.mlr.press/v235/zhang24d.html
|
Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang, Yao Hu
|
https://proceedings.mlr.press/v235/zhang24d.html
|
ICML 2024
|
While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal & Zhang (2022) recently proposed a variant that replaces risk with excess risk. Compared to DRO, the new formulation—minimax excess risk optimization (MERO) has the advantage of suppressing the effect of heterogeneous noise in different distributions. However, the choice of excess risk leads to a very challenging minimax optimization problem, and currently there exists only an inefficient algorithm for empirical MERO. In this paper, we develop efficient stochastic approximation approaches which directly target MERO. Specifically, we leverage techniques from stochastic convex optimization to estimate the minimal risk of every distribution, and solve MERO as a stochastic convex-concave optimization (SCCO) problem with biased gradients. The presence of bias makes existing theoretical guarantees of SCCO inapplicable, and fortunately, we demonstrate that the bias, caused by the estimation error of the minimal risk, is under-control. Thus, MERO can still be optimized with a nearly optimal convergence rate. Moreover, we investigate a practical scenario where the quantity of samples drawn from each distribution may differ, and propose a stochastic approach that delivers distribution-dependent convergence rates.
|
https://proceedings.mlr.press/v235/zhang24e.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24e/zhang24e.pdf
|
https://openreview.net/forum?id=ZoTIdyExx6
|
Discounted Adaptive Online Learning: Towards Better Regularization
|
https://proceedings.mlr.press/v235/zhang24e.html
|
Zhiyu Zhang, David Bombara, Heng Yang
|
https://proceedings.mlr.press/v235/zhang24e.html
|
ICML 2024
|
We study online learning in adversarial nonstationary environments. Since the future can be very different from the past, a critical challenge is to gracefully forget the history while new data comes in. To formalize this intuition, we revisit the discounted regret in online convex optimization, and propose an adaptive (i.e., instance optimal), FTRL-based algorithm that improves the widespread non-adaptive baseline – gradient descent with a constant learning rate. From a practical perspective, this refines the classical idea of regularization in lifelong learning: we show that designing better regularizers can be guided by the principled theory of adaptive online optimization. Complementing this result, we also consider the (Gibbs & Candes, 2021)-style online conformal prediction problem, where the goal is to sequentially predict the uncertainty sets of a black-box machine learning model. We show that the FTRL nature of our algorithm can simplify the conventional gradient-descent-based analysis, leading to instance-dependent performance guarantees.
|
https://proceedings.mlr.press/v235/zhang24f.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24f/zhang24f.pdf
|
https://openreview.net/forum?id=wWdkNkUY8k
|
Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
|
https://proceedings.mlr.press/v235/zhang24f.html
|
Yuelin Zhang, Jiacheng Cen, Jiaqi Han, Zhiqiang Zhang, Jun Zhou, Wenbing Huang
|
https://proceedings.mlr.press/v235/zhang24f.html
|
ICML 2024
|
Equivariant Graph Neural Networks (GNNs) have made remarkable success in a variety of scientific applications. However, existing equivariant GNNs encounter the efficiency issue for large geometric graphs and perform poorly if the input is reduced to sparse local graph for speed acceleration. In this paper, we propose FastEGNN, an enhanced model of equivariant GNNs on large geometric graphs. The central idea is leveraging a small ordered set of virtual nodes to approximate the large unordered graph of real nodes. In particular, we distinguish the message passing and aggregation for different virtual node to encourage the mutual distinctiveness, and minimize the Maximum Mean Discrepancy (MMD) between virtual and real coordinates to realize the global distributedness. FastEGNN meets all necessary E(3) symmetries, with certain universal expressivity assurance as well. Our experiments on N-body systems (100 nodes), proteins (800 nodes) and water-3D (8000 nodes), demonstrate that FastEGNN achieves a promising balance between accuracy and efficiency, and outperforms EGNN in accuracy even after dropping all edges in real systems like proteins and water-3D.
|
https://proceedings.mlr.press/v235/zhang24g.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24g/zhang24g.pdf
|
https://openreview.net/forum?id=5S8ukkEQr2
|
Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
|
https://proceedings.mlr.press/v235/zhang24g.html
|
Tonghe Zhang, Yu Chen, Longbo Huang
|
https://proceedings.mlr.press/v235/zhang24g.html
|
ICML 2024
|
This work pioneers regret analysis of risk-sensitive reinforcement learning in partially observable environments with hindsight observation, addressing a gap in theoretical exploration. We introduce a novel formulation that integrates hindsight observations into a Partially Observable Markov Decision Process (POMDP) framework, where the goal is to optimize accumulated reward under the entropic risk measure. We develop the first provably efficient RL algorithm tailored for this setting. We also prove by rigorous analysis that our algorithm achieves polynomial regret $\tilde{O}\left(\frac{e^{|{\gamma}|H}-1}{|{\gamma}|H}H^2\sqrt{KHS^2OA}\right)$, which outperforms or matches existing upper bounds when the model degenerates to risk-neutral or fully observable settings. We adopt the method of change-of-measure and develop a novel analytical tool of beta vectors to streamline mathematical derivations. These techniques are of particular interest to the theoretical study of reinforcement learning.
|
https://proceedings.mlr.press/v235/zhang24h.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24h/zhang24h.pdf
|
https://openreview.net/forum?id=OkChMnjF6s
|
Verification of Machine Unlearning is Fragile
|
https://proceedings.mlr.press/v235/zhang24h.html
|
Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li
|
https://proceedings.mlr.press/v235/zhang24h.html
|
ICML 2024
|
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearning and avoid potential dishonesty by model providers, various verification strategies have been proposed. These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model. However, our understanding of the safety issues of machine unlearning verification remains nascent. In this paper, we explore the novel research question of whether model providers can circumvent verification strategies while retaining the information of data supposedly unlearned. Our investigation leads to a pessimistic answer: the verification of machine unlearning is fragile. Specifically, we categorize the current verification strategies regarding potential dishonesty among model providers into two types. Subsequently, we introduce two novel adversarial unlearning processes capable of circumventing both types. We validate the efficacy of our methods through theoretical analysis and empirical experiments using real-world datasets. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
|
https://proceedings.mlr.press/v235/zhang24i.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24i/zhang24i.pdf
|
https://openreview.net/forum?id=fM9xTkpAdu
|
Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems
|
https://proceedings.mlr.press/v235/zhang24i.html
|
Bonan Zhang, Chia-Yu Chen, Naveen Verma
|
https://proceedings.mlr.press/v235/zhang24i.html
|
ICML 2024
|
In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results back to the digital domain. However, ADCs maintain computational efficiency by having limited precision, leading to substantial quantization errors in compute outputs. This work proposes RAOQ (Reshape and Adapt for Output Quantization) to overcome this issue, which comprises two classes of mechanisms including: 1) mitigating ADC quantization error by adjusting the statistics of activations and weights, through an activation-shifting approach (A-shift) and a weight reshaping technique (W-reshape); 2) adapting AI models to better tolerate ADC quantization through a bit augmentation method (BitAug), complemented by the introduction of ADC-LoRA, a low-rank approximation technique, to reduce the training overhead. RAOQ demonstrates consistently high performance across different scales and domains of neural network models for computer vision and natural language processing (NLP) tasks at various bit precisions, achieving state-of-the-art results with practical IMC implementations.
|
https://proceedings.mlr.press/v235/zhang24j.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24j/zhang24j.pdf
|
https://openreview.net/forum?id=dh8k41g775
|
LQER: Low-Rank Quantization Error Reconstruction for LLMs
|
https://proceedings.mlr.press/v235/zhang24j.html
|
Cheng Zhang, Jianyi Cheng, George Anthony Constantinides, Yiren Zhao
|
https://proceedings.mlr.press/v235/zhang24j.html
|
ICML 2024
|
Post-training quantization of Large Language Models (LLMs) is challenging. In this work, we introduce Low-rank Quantization Error Reduction (LQER), which combines quantization and low-rank approximation to recover the model capability. LQER leverages an activation-induced scale matrix to drive the singular value distribution of quantization error towards a desirable distribution, which enables nearly-lossless W4A8 quantization on various LLMs and downstream tasks without the need for knowledge distillation, grid search, or gradient-based iterative optimization. Unlike existing methods, the computation pattern of LQER eliminates the need for specialized Scatter and Gather processes to collect high-precision weights from irregular memory locations. Our W4A8 LLMs achieve near-lossless performance on six popular downstream tasks, while using $1.36 \times$ fewer hardware resources than the leading state-of-the-art method. We will open-source our framework at https://github.com/ChengZhang-98/lqer
|
https://proceedings.mlr.press/v235/zhang24k.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24k/zhang24k.pdf
|
https://openreview.net/forum?id=NKirMgDsut
|
Random Scaling and Momentum for Non-smooth Non-convex Optimization
|
https://proceedings.mlr.press/v235/zhang24k.html
|
Qinzi Zhang, Ashok Cutkosky
|
https://proceedings.mlr.press/v235/zhang24k.html
|
ICML 2024
|
Training neural networks requires optimizing a loss function that may be highly irregular, and in particular neither convex nor smooth. Popular training algorithms are based on stochastic gradient descent with momentum (SGDM), for which classical analysis applies only if the loss is either convex or smooth. We show that a very small modification to SGDM closes this gap: simply scale the update at each time point by an exponentially distributed random scalar. The resulting algorithm achieves optimal convergence guarantees. Intriguingly, this result is not derived by a specific analysis of SGDM: instead, it falls naturally out of a more general framework for converting online convex optimization algorithms to non-convex optimization algorithms.
|
https://proceedings.mlr.press/v235/zhang24l.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24l/zhang24l.pdf
|
https://openreview.net/forum?id=1mf1ISuyS3
|
Towards Certified Unlearning for Deep Neural Networks
|
https://proceedings.mlr.press/v235/zhang24l.html
|
Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li
|
https://proceedings.mlr.press/v235/zhang24l.html
|
ICML 2024
|
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees. However, its application to deep neural networks (DNNs), known for their highly nonconvex nature, still poses challenges. To bridge the gap between certified unlearning and DNNs, we propose several simple techniques to extend certified unlearning methods to nonconvex objectives. To reduce the time complexity, we develop an efficient computation method by inverse Hessian approximation without compromising certification guarantees. In addition, we extend our discussion of certification to nonconvergence training and sequential unlearning, considering that real-world users can send unlearning requests at different time points. Extensive experiments on three real-world datasets demonstrate the efficacy of our method and the advantages of certified unlearning in DNNs.
|
https://proceedings.mlr.press/v235/zhang24m.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24m/zhang24m.pdf
|
https://openreview.net/forum?id=2rPoTgEmjV
|
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
|
https://proceedings.mlr.press/v235/zhang24m.html
|
Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang
|
https://proceedings.mlr.press/v235/zhang24m.html
|
ICML 2024
|
In recent years, the rise of generative self-supervised learning (SSL) paradigms has exhibited impressive performance across visual, language, and multi-modal domains. While the varied designs of generative SSL objectives lead to distinct properties in downstream tasks, a theoretical understanding of these differences remains largely unexplored. In this paper, we establish the first theoretical comparisons between two leading generative SSL paradigms: autoregressive SSL and masked SSL. Through establishing theoretical frameworks, we elucidate the strengths and limitations of autoregressive and masked SSL within the primary evaluation tasks of classification and content generation. Our findings demonstrate that in classification tasks, the flexibility of targeted tokens in masked SSL fosters more inter-sample connections compared to the fixed position of target tokens in autoregressive SSL, which yields superior clustering performance. In content generation tasks, the misalignment between the flexible lengths of test samples and the fixed length of unmasked texts in masked SSL (vs. flexible lengths of conditional texts in autoregressive SSL) hinders its generation performance. To leverage each other’s strengths and mitigate weaknesses, we propose diversity-enhanced autoregressive and variable-length masked objectives, which substantially improve the classification performance of autoregressive SSL and the generation performance of masked SSL. Code is available at https://github.com/PKU-ML/LookAheadLookAround.
|
https://proceedings.mlr.press/v235/zhang24n.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24n/zhang24n.pdf
|
https://openreview.net/forum?id=LCTmppB165
|
CaM: Cache Merging for Memory-efficient LLMs Inference
|
https://proceedings.mlr.press/v235/zhang24n.html
|
Yuxin Zhang, Yuxuan Du, Gen Luo, Yunshan Zhong, Zhenyu Zhang, Shiwei Liu, Rongrong Ji
|
https://proceedings.mlr.press/v235/zhang24n.html
|
ICML 2024
|
Despite the exceptional performance of Large Language Models (LLMs), the substantial volume of key-value (KV) pairs cached during inference presents a barrier to their efficient deployment. To ameliorate this, recent works have aimed to selectively eliminate these caches, informed by the attention scores of associated tokens. However, such cache eviction invariably leads to output perturbation, regardless of the token choice. This perturbation escalates with the compression ratio, which can precipitate a marked deterioration in LLM inference performance. This paper introduces Cache Merging (CaM) as a solution to mitigate this challenge. CaM adaptively merges to-be-evicted caches into the remaining ones, employing a novel sampling strategy governed by the prominence of attention scores within discarded locations. In this manner, CaM enables memory-efficient LLMs to preserve critical token information, even obviating the need to maintain their corresponding caches. Extensive experiments utilizing LLaMA, OPT, and GPT-NeoX across various benchmarks corroborate CaM’s proficiency in bolstering the performance of memory-efficient LLMs. Code is released at https://github.com/zyxxmu/cam.
|
https://proceedings.mlr.press/v235/zhang24o.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24o/zhang24o.pdf
|
https://openreview.net/forum?id=bM2s12t4hR
|
Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models
|
https://proceedings.mlr.press/v235/zhang24o.html
|
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
|
https://proceedings.mlr.press/v235/zhang24o.html
|
ICML 2024
|
Watermarking generative models consists of planting a statistical signal (watermark) in a model’s output so that it can be later verified that the output was generated by the given model. A strong watermarking scheme satisfies the property that a computationally bounded attacker cannot erase the watermark without causing significant quality degradation. In this paper, we study the (im)possibility of strong watermarking schemes. We prove that, under well-specified and natural assumptions, strong watermarking is impossible to achieve. This holds even in the private detection algorithm setting, where the watermark insertion and detection algorithms share a secret key, unknown to the attacker. To prove this result, we introduce a generic efficient watermark attack; the attacker is not required to know the private key of the scheme or even which scheme is used. Our attack is based on two assumptions: (1) The attacker has access to a "quality oracle" that can evaluate whether a candidate output is a high-quality response to a prompt, and (2) The attacker has access to a "perturbation oracle" which can modify an output with a nontrivial probability of maintaining quality, and which induces an efficiently mixing random walk on high-quality outputs. We argue that both assumptions can be satisfied in practice by an attacker with weaker computational capabilities than the watermarked model itself, to which the attacker has only black-box access. Furthermore, our assumptions will likely only be easier to satisfy over time as models grow in capabilities and modalities. We demonstrate the feasibility of our attack by instantiating it to attack three existing watermarking schemes for large language models: Kirchenbauer et al. (2023), Kuditipudi et al. (2023), and Zhao et al. (2023), and include preliminary results on vision-language models. The same attack successfully removes the watermarks planted by all schemes, with only minor quality degradation.
|
https://proceedings.mlr.press/v235/zhang24p.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24p/zhang24p.pdf
|
https://openreview.net/forum?id=j4HtfTqr0f
|
MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness
|
https://proceedings.mlr.press/v235/zhang24p.html
|
Yahong Zhang, Chenchen Fan, Donghui Chen, Congrui Li, Wenli Ouyang, Mingda Zhu, Junchi Yan
|
https://proceedings.mlr.press/v235/zhang24p.html
|
ICML 2024
|
Machine learning (ML) has been actively adopted in Linear Programming (LP) and Mixed-Integer Linear Programming (MILP), whose potential is hindered by instance scarcity. Current synthetic instance generation methods often fall short in closely mirroring the distribution of original datasets or ensuring the feasibility and boundedness of the generated data — a critical requirement for obtaining reliable supervised labels in model training. In this paper, we present a diffusion-based LP/MILP instance generative framework called MILP-FBGen. It strikes a balance between structural similarity and novelty while maintaining feasibility/boundedness via a meticulously designed structure-preserving generation module and a feasibility/boundedness-constrained sampling module. Our method shows superiority on two fronts: 1) preservation of key properties (hardness, feasibility, and boundedness) of LP/MILP instances, and 2) enhanced performance on downstream tasks. Extensive studies show two-fold superiority that our method ensures higher distributional similarity and 100% feasibility in both easy and hard datasets, surpassing current state-of-the-art techniques.
|
https://proceedings.mlr.press/v235/zhang24q.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24q/zhang24q.pdf
|
https://openreview.net/forum?id=pDoAjdrMf0
|
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
|
https://proceedings.mlr.press/v235/zhang24q.html
|
Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang
|
https://proceedings.mlr.press/v235/zhang24q.html
|
ICML 2024
|
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward function. This Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like Q-learning. However, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN). This paper studies the provable knowledge transfer using SFs-DQN in transfer RL problems. We establish the first convergence analysis with provable generalization guarantees for SF-DQN with GPI. The theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better generalization. Numerical experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings.
|
https://proceedings.mlr.press/v235/zhang24r.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24r/zhang24r.pdf
|
https://openreview.net/forum?id=xW79geE0RA
|
Model-based Reinforcement Learning for Parameterized Action Spaces
|
https://proceedings.mlr.press/v235/zhang24r.html
|
Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris
|
https://proceedings.mlr.press/v235/zhang24r.html
|
ICML 2024
|
We propose a novel model-based reinforcement learning algorithm—Dynamics Learning and predictive control with Parameterized Actions (DLPA)—for Parameterized Action Markov Decision Processes (PAMDPs). The agent learns a parameterized-action-conditioned dynamics model and plans with a modified Model Predictive Path Integral control. We theoretically quantify the difference between the generated trajectory and the optimal trajectory during planning in terms of the value they achieved through the lens of Lipschitz Continuity. Our empirical results on several standard benchmarks show that our algorithm achieves superior sample efficiency and asymptotic performance than state-of-the-art PAMDP methods.
|
https://proceedings.mlr.press/v235/zhang24s.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24s/zhang24s.pdf
|
https://openreview.net/forum?id=MsnJl6JkZS
|
Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
|
https://proceedings.mlr.press/v235/zhang24s.html
|
Jiewei Zhang, Song Guo, Peiran Dong, Jie Zhang, Ziming Liu, Yue Yu, Xiao-Ming Wu
|
https://proceedings.mlr.press/v235/zhang24s.html
|
ICML 2024
|
Recent diffusion models have manifested extraordinary capabilities in generating high-quality, diverse, and innovative images guided by textual prompts. Nevertheless, these state-of-the-art models may encounter the challenge of concept bleeding when generating images with multiple entities or attributes in the prompt, leading to the unanticipated merging or overlapping of distinct objects in the synthesized result. The current work exploits auxiliary networks to produce mask-constrained regions for entities, necessitating the training of an object detection network. In this paper, we investigate the bleeding reason and find that the cross-attention map associated with a specific entity or attribute tends to extend beyond its intended focus, encompassing the background or other unrelated objects and thereby acting as the primary source of concept bleeding. Motivated by this, we propose Entity Localization and Anchoring (ELA) to drive the entity to concentrate on the expected region accurately during inference, eliminating the necessity for training. Specifically, we initially identify the region corresponding to each entity and subsequently employ a tailored loss function to anchor entities within their designated positioning areas. Extensive experiments demonstrate its superior capability in precisely generating multiple objects as specified in the textual prompts.
|
https://proceedings.mlr.press/v235/zhang24t.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24t/zhang24t.pdf
|
https://openreview.net/forum?id=CpcaL75UgY
|
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
|
https://proceedings.mlr.press/v235/zhang24t.html
|
Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
|
https://proceedings.mlr.press/v235/zhang24t.html
|
ICML 2024
|
To improve the ability of the large language model (LLMs) to tackle complex reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs to reason step-by-step, enabling problem solving from simple to complex. State-of-the-art methods for generating such a chain involve interactive collaboration, where the learner generates candidate intermediate thoughts, evaluated by the LLM, guiding the generation of subsequent thoughts. However, a widespread yet understudied problem is that the evaluation from the LLM is typically noisy and unreliable, potentially misleading the generation process in selecting promising intermediate thoughts. In this paper, motivated by Vapnik’s principle, we use pairwise-comparison evaluation instead of point-wise scoring to search for promising intermediate thoughts with the noisy feedback from the LLM. In each round, we randomly pair intermediate thoughts and directly prompt the LLM to select the more promising one from each pair, allowing us to identify the most promising thoughts through an iterative process. To further alleviate the noise in the comparison, we incorporate techniques from ensemble learning and dueling bandits, proposing two variants of the algorithm. Experiments on three real-world tasks demonstrate the effectiveness of our proposed algorithm and verify the rationale of the pairwise comparison mechanism.
|
https://proceedings.mlr.press/v235/zhang24u.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24u/zhang24u.pdf
|
https://openreview.net/forum?id=fwxnHViGNj
|
Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
|
https://proceedings.mlr.press/v235/zhang24u.html
|
Guanhua Zhang, Moritz Hardt
|
https://proceedings.mlr.press/v235/zhang24u.html
|
ICML 2024
|
We examine multi-task benchmarks in machine learning through the lens of social choice theory. We draw an analogy between benchmarks and electoral systems, where models are candidates and tasks are voters. This suggests a distinction between cardinal and ordinal benchmark systems. The former aggregate numerical scores into one model ranking; the latter aggregate rankings for each task. We apply Arrow’s impossibility theorem to ordinal benchmarks to highlight the inherent limitations of ordinal systems, particularly their sensitivity to the inclusion of irrelevant models. Inspired by Arrow’s theorem, we empirically demonstrate a strong trade-off between diversity and sensitivity to irrelevant changes in existing multi-task benchmarks. Our result is based on new quantitative measures of diversity and sensitivity that we introduce. Sensitivity quantifies the impact that irrelevant changes to tasks have on a benchmark. Diversity captures the degree of disagreement in model rankings across tasks. We develop efficient approximation algorithms for both measures, as exact computation is computationally challenging. Through extensive experiments on seven cardinal benchmarks and eleven ordinal benchmarks, we demonstrate a clear trade-off between diversity and stability: The more diverse a multi-task benchmark, the more sensitive to trivial changes it is. Additionally, we show that the aggregated rankings of existing benchmarks are highly unstable under irrelevant changes. The codes and data are available at https://socialfoundations.github.io/benchbench/.
|
https://proceedings.mlr.press/v235/zhang24v.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24v/zhang24v.pdf
|
https://openreview.net/forum?id=eN1T7I7OpZ
|
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
|
https://proceedings.mlr.press/v235/zhang24v.html
|
Chen Zhang, Qiang He, Yuan Zhou, Elvis S. Liu, Hong Wang, Jian Zhao, Yang Wang
|
https://proceedings.mlr.press/v235/zhang24v.html
|
ICML 2024
|
Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a wide range of game genres. However, existing research primarily focuses on optimizing DRL competence rather than addressing the challenge of prolonged player interaction. In this paper, we propose a practical DRL agent system for fighting games named Shūkai, which has been successfully deployed to Naruto Mobile, a popular fighting game with over 100 million registered users. Shūkai quantifies the state to enhance generalizability, introducing Heterogeneous League Training (HELT) to achieve balanced competence, generalizability, and training efficiency. Furthermore, Shūkai implements specific rewards to align the agent’s behavior with human expectations. Shūkai’s ability to generalize is demonstrated by its consistent competence across all characters, even though it was trained on only 13% of them. Additionally, HELT exhibits a remarkable 22% improvement in sample efficiency. Shūkai serves as a valuable training partner for players in Naruto Mobile, enabling them to enhance their abilities and skills.
|
https://proceedings.mlr.press/v235/zhang24w.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24w/zhang24w.pdf
|
https://openreview.net/forum?id=2B2U5kkGUA
|
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
|
https://proceedings.mlr.press/v235/zhang24w.html
|
Yihao Zhang, Hangzhou He, Jingyu Zhu, Huanran Chen, Yifei Wang, Zeming Wei
|
https://proceedings.mlr.press/v235/zhang24w.html
|
ICML 2024
|
Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, yet suffers from inevitably decreased clean accuracy. Instead of perturbing the samples, Sharpness-Aware Minimization (SAM) perturbs the model weights during training to find a more flat loss landscape and improve generalization. However, as SAM is designed for better clean accuracy, its effectiveness in enhancing adversarial robustness remains unexplored. In this work, considering the duality between SAM and AT, we investigate the adversarial robustness derived from SAM. Intriguingly, we find that using SAM alone can improve adversarial robustness. To understand this unexpected property of SAM, we first provide empirical and theoretical insights into how SAM can implicitly learn more robust features, and conduct comprehensive experiments to show that SAM can improve adversarial robustness notably without sacrificing any clean accuracy, shedding light on the potential of SAM to be a substitute for AT when accuracy comes at a higher priority. Code is available at https://github.com/weizeming/SAM_AT.
|
https://proceedings.mlr.press/v235/zhang24x.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24x/zhang24x.pdf
|
https://openreview.net/forum?id=cFDaYtZR4u
|
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
|
https://proceedings.mlr.press/v235/zhang24x.html
|
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma
|
https://proceedings.mlr.press/v235/zhang24x.html
|
ICML 2024
|
Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks. However, a gap persists in complex tasks such as causal inference, primarily due to challenges associated with intricate reasoning steps and high numerical precision requirements. In this work, we take a first step towards building causally-aware foundation models for treatment effect estimations. We propose a novel, theoretically justified method called Causal Inference with Attention (CInA), which utilizes multiple unlabeled datasets to perform self-supervised causal learning, and subsequently enables zero-shot causal inference on unseen tasks with new data. This is based on our theoretical results that demonstrate the primal-dual connection between optimal covariate balancing and self-attention, facilitating zero-shot causal inference through the final layer of a trained transformer-type architecture. We demonstrate empirically that CInA effectively generalizes to out-of-distribution datasets and various real-world datasets, matching or even surpassing traditional per-dataset methodologies. These results provide compelling evidence that our method has the potential to serve as a stepping stone for the development of causal foundation models.
|
https://proceedings.mlr.press/v235/zhang24y.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24y/zhang24y.pdf
|
https://openreview.net/forum?id=C4nalr0DoE
|
Parameter-Efficient Fine-Tuning with Controls
|
https://proceedings.mlr.press/v235/zhang24y.html
|
Chi Zhang, Cheng Jingpu, Yanyu Xu, Qianxiao Li
|
https://proceedings.mlr.press/v235/zhang24y.html
|
ICML 2024
|
In contrast to the prevailing interpretation of Low-Rank Adaptation (LoRA) as a means of simulating weight changes in model adaptation, this paper introduces an alternative perspective by framing it as a control process. Specifically, we conceptualize lightweight matrices in LoRA as control modules tasked with perturbing the original, complex, yet frozen blocks on downstream tasks. Building upon this new understanding, we conduct a thorough analysis on the controllability of these modules, where we identify and establish sufficient conditions that facilitate their effective integration into downstream controls. Moreover, the control modules are redesigned by incorporating nonlinearities through a parameter-free attention mechanism. This modification allows for the intermingling of tokens within the controllers, enhancing the adaptability and performance of the system. Empirical findings substantiate that, without introducing any additional parameters, this approach surpasses the existing LoRA algorithms across all assessed datasets and rank configurations.
|
https://proceedings.mlr.press/v235/zhang24z.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24z/zhang24z.pdf
|
https://openreview.net/forum?id=HbdeEGVfEN
|
Deep Regression Representation Learning with Topology
|
https://proceedings.mlr.press/v235/zhang24z.html
|
Shihao Zhang, Kenji Kawaguchi, Angela Yao
|
https://proceedings.mlr.press/v235/zhang24z.html
|
ICML 2024
|
Most works studying representation learning focus only on classification and neglect regression. Yet, the learning objectives and, therefore, the representation topologies of the two tasks are fundamentally different: classification targets class separation, leading to disconnected representations, whereas regression requires ordinality with respect to the target, leading to continuous representations. We thus wonder how the effectiveness of a regression representation is influenced by its topology, with evaluation based on the Information Bottleneck (IB) principle. The IB principle is an important framework that provides principles for learning effective representations. We establish two connections between it and the topology of regression representations. The first connection reveals that a lower intrinsic dimension of the feature space implies a reduced complexity of the representation $Z$. This complexity can be quantified as the conditional entropy of $Z$ on the target $Y$, and serves as an upper bound on the generalization error. The second connection suggests a feature space that is topologically similar to the target space will better align with the IB principle. Based on these two connections, we introduce PH-Reg, a regularizer specific to regression that matches the intrinsic dimension and topology of the feature space with the target space. Experiments on synthetic and real-world regression tasks demonstrate the benefits of PH-Reg. Code: https://github.com/needylove/PH-Reg.
|
https://proceedings.mlr.press/v235/zhang24aa.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24aa/zhang24aa.pdf
|
https://openreview.net/forum?id=CTEMHDSwIj
|
Understanding Unimodal Bias in Multimodal Deep Linear Networks
|
https://proceedings.mlr.press/v235/zhang24aa.html
|
Yedi Zhang, Peter E. Latham, Andrew M Saxe
|
https://proceedings.mlr.press/v235/zhang24aa.html
|
ICML 2024
|
Using multiple input streams simultaneously to train multimodal neural networks is intuitively advantageous but practically challenging. A key challenge is unimodal bias, where a network overly relies on one modality and ignores others during joint training. We develop a theory of unimodal bias with multimodal deep linear networks to understand how architecture and data statistics influence this bias. This is the first work to calculate the duration of the unimodal phase in learning as a function of the depth at which modalities are fused within the network, dataset statistics, and initialization. We show that the deeper the layer at which fusion occurs, the longer the unimodal phase. A long unimodal phase can lead to a generalization deficit and permanent unimodal bias in the overparametrized regime. Our results, derived for multimodal linear networks, extend to nonlinear networks in certain settings. Taken together, this work illuminates pathologies of multimodal learning under joint training, showing that late and intermediate fusion architectures can give rise to long unimodal phases and permanent unimodal bias. Our code is available at: https://yedizhang.github.io/unimodal-bias.html.
|
https://proceedings.mlr.press/v235/zhang24ab.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ab/zhang24ab.pdf
|
https://openreview.net/forum?id=zFHaB7KESM
|
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
|
https://proceedings.mlr.press/v235/zhang24ab.html
|
Thomas Tck Zhang, Bruce D Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni
|
https://proceedings.mlr.press/v235/zhang24ab.html
|
ICML 2024
|
A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many sources. However, many practical domains involve data that are non-identically distributed across sources, and possibly statistically dependent within its source, violating vital assumptions in existing theoretical studies of representation learning. Toward addressing these issues, we establish statistical guarantees for learning general nonlinear representations from multiple data sources that admit different input distributions and possibly dependent data. Specifically, we study the sample-complexity of learning $T+1$ functions $f_\star^{(t)} \circ g_\star$ from a function class $\mathcal{F} \times \mathcal{G}$, where $f_\star^{(t)}$ are task specific linear functions and $g_\star$ is a shared non-linear representation. An approximate representation $\hat g$ is estimated using $N$ samples from each of $T$ source tasks, and a fine-tuning function $\hat f^{(0)}$ is fit using $N’$ samples from a target task passed through $\hat g$. Our results show that the excess risk of the estimate $\hat f^{(0)} \circ \hat g$ on the target task decays as $\tilde{\mathcal{O}}\Big(\frac{\mathrm{C}(\mathcal{G})}{N T} + \frac{\text{dim}(\mathcal{F})}{N’}\Big)$, where $\mathrm{C}(\mathcal{G})$ denotes the complexity of $\mathcal{G}$. Notably, our rates match that of the iid setting, while requiring fewer samples per task than prior analysis and admitting no dependence on the mixing time. We support our analysis with numerical experiments performing imitation learning over non-linear dynamical systems.
|
https://proceedings.mlr.press/v235/zhang24ac.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ac/zhang24ac.pdf
|
https://openreview.net/forum?id=wUgTnf918v
|
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
|
https://proceedings.mlr.press/v235/zhang24ac.html
|
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
|
https://proceedings.mlr.press/v235/zhang24ac.html
|
ICML 2024
|
The massive developments of generative model frameworks require principled methods for the evaluation of a model’s novelty compared to a reference dataset. While the literature has extensively studied the evaluation of the quality, diversity, and generalizability of generative models, the assessment of a model’s novelty compared to a reference model has not been adequately explored in the machine learning community. In this work, we focus on the novelty assessment for multi-modal distributions and attempt to address the following differential clustering task: Given samples of a generative model $P_\mathcal{G}$ and a reference model $P_\mathrm{ref}$, how can we discover the sample types expressed by $P_\mathcal{G}$ more frequently than in $P_\mathrm{ref}$? We introduce a spectral approach to the differential clustering task and propose the Kernel-based Entropic Novelty (KEN) score to quantify the mode-based novelty of $P_\mathcal{G}$ with respect to $P_\mathrm{ref}$. We analyze the KEN score for mixture distributions with well-separable components and develop a kernel-based method to compute the KEN score from empirical data. We support the KEN framework by presenting numerical results on synthetic and real image datasets, indicating the framework’s effectiveness in detecting novel modes and comparing generative models. The paper’s code is available at: github.com/buyeah1109/KEN.
|
https://proceedings.mlr.press/v235/zhang24ad.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ad/zhang24ad.pdf
|
https://openreview.net/forum?id=THPjMr2r0S
|
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
|
https://proceedings.mlr.press/v235/zhang24ad.html
|
Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
|
https://proceedings.mlr.press/v235/zhang24ad.html
|
ICML 2024
|
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard. Yet, as LLMs grow in size, the substantial memory overhead from back-propagation (BP) for FO gradient computation presents a significant challenge. Addressing this issue is crucial, especially for applications like on-device training where memory efficiency is paramount. This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during LLM fine-tuning, building on the initial concept introduced by (Malladi et al., 2023). Unlike traditional ZO-SGD methods, ou让work expands the exploration to a wider array of ZO optimization techniques, through a comprehensive, first-of-its-kind benchmarking study across five LLM families, three task complexities, and five fine-tuning schemes. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance. We further introduce novel enhancements to ZO optimization, including block-wise descent, hybrid training, and gradient sparsity. Our study offers a promising direction for achieving further memory-efficient LLM fine-tuning. Codes to reproduce all our experiments will be made public.
|
https://proceedings.mlr.press/v235/zhang24ae.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ae/zhang24ae.pdf
|
https://openreview.net/forum?id=xcyKKACmSd
|
S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video
|
https://proceedings.mlr.press/v235/zhang24ae.html
|
Hao Zhang, Fang Li, Samyak Rawlekar, Narendra Ahuja
|
https://proceedings.mlr.press/v235/zhang24ae.html
|
ICML 2024
|
Reconstructing dynamic articulated objects from a singular monocular video is challenging, requiring joint estimation of shape, motion, and camera parameters from limited views. Current methods typically demand extensive computational resources and training time, and require additional human annotations such as predefined parametric models, camera poses, and key points, limiting their generalizability. We propose Synergistic Shape and Skeleton Optimization (S3O), a novel two-phase method that forgoes these prerequisites and efficiently learns parametric models including visible shapes and underlying skeletons. Conventional strategies typically learn all parameters simultaneously, leading to interdependencies where a single incorrect prediction can result in significant errors. In contrast, S3O adopts a phased approach: it first focuses on learning coarse parametric models, then progresses to motion learning and detail addition. This method substantially lowers computational complexity and enhances robustness in reconstruction from limited viewpoints, all without requiring additional annotations. To address the current inadequacies in 3D reconstruction from monocular video benchmarks, we collected the PlanetZoo dataset. Our experimental evaluations on standard benchmarks and the PlanetZoo dataset affirm that S3O provides more accurate 3D reconstruction, and plausible skeletons, and reduces the training time by approximately 60% compared to the state-of-the-art, thus advancing the state of the art in dynamic object reconstruction.
|
https://proceedings.mlr.press/v235/zhang24af.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24af/zhang24af.pdf
|
https://openreview.net/forum?id=QJkG8Mln72
|
DPZero: Private Fine-Tuning of Language Models without Backpropagation
|
https://proceedings.mlr.press/v235/zhang24af.html
|
Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
|
https://proceedings.mlr.press/v235/zhang24af.html
|
ICML 2024
|
The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy. First, as the size of LLMs continues to grow, the memory demands of gradient-based training methods via backpropagation become prohibitively high. Second, given the tendency of LLMs to memorize training data, it is important to protect potentially sensitive information in the fine-tuning data from being regurgitated. Zeroth-order methods, which rely solely on forward passes, substantially reduce memory consumption during training. However, directly combining them with standard differentially private gradient descent suffers more as model size grows. To bridge this gap, we introduce DPZero, a novel private zeroth-order algorithm with nearly dimension-independent rates. The memory efficiency of DPZero is demonstrated in privately fine-tuning RoBERTa and OPT on several downstream tasks. Our code is available at https://github.com/Liang137/DPZero.
|
https://proceedings.mlr.press/v235/zhang24ag.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ag/zhang24ag.pdf
|
https://openreview.net/forum?id=jXn1qIcjyG
|
Conditional Language Learning with Context
|
https://proceedings.mlr.press/v235/zhang24ag.html
|
Xiao Zhang, Miao Li, Ji Wu
|
https://proceedings.mlr.press/v235/zhang24ag.html
|
ICML 2024
|
Language models can learn sophisticated language understanding skills from fitting raw text. They also unselectively learn useless corpus statistics and biases, especially during finetuning on domain-specific corpora. In this paper, we propose a simple modification to causal language modeling called conditional finetuning, which performs language modeling conditioned on a context. We show that a context can "explain away" certain corpus statistics and make the model avoid learning them. In this fashion, conditional finetuning achieves selective learning from a corpus, learning knowledge useful for downstream tasks while avoiding learning useless corpus statistics like topic biases. This selective learning effect leads to less forgetting and better stability-plasticity tradeoff in domain finetuning, potentially benefitting lifelong learning with language models.
|
https://proceedings.mlr.press/v235/zhang24ah.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ah/zhang24ah.pdf
|
https://openreview.net/forum?id=WLGWMDtj8L
|
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
|
https://proceedings.mlr.press/v235/zhang24ah.html
|
Wanpeng Zhang, Yilin Li, Boyu Yang, Zongqing Lu
|
https://proceedings.mlr.press/v235/zhang24ah.html
|
ICML 2024
|
In real-world scenarios, the application of reinforcement learning is significantly challenged by complex non-stationarity. Most existing methods attempt to model changes in the environment explicitly, often requiring impractical prior knowledge of environments. In this paper, we propose a new perspective, positing that non-stationarity can propagate and accumulate through complex causal relationships during state transitions, thereby compounding its sophistication and affecting policy learning. We believe that this challenge can be more effectively addressed by implicitly tracing the causal origin of non-stationarity. To this end, we introduce the Causal-Origin REPresentation (COREP) algorithm. COREP primarily employs a guided updating mechanism to learn a stable graph representation for the state, termed as causal-origin representation. By leveraging this representation, the learned policy exhibits impressive resilience to non-stationarity. We supplement our approach with a theoretical analysis grounded in the causal interpretation for non-stationary reinforcement learning, advocating for the validity of the causal-origin representation. Experimental results further demonstrate the superior performance of COREP over existing methods in tackling non-stationarity problems. The code is available at https://github.com/PKU-RL/COREP.
|
https://proceedings.mlr.press/v235/zhang24ai.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ai/zhang24ai.pdf
|
https://openreview.net/forum?id=qRtM5EqE9l
|
BLO-SAM: Bi-level Optimization Based Finetuning of the Segment Anything Model for Overfitting-Preventing Semantic Segmentation
|
https://proceedings.mlr.press/v235/zhang24ai.html
|
Li Zhang, Youwei Liang, Ruiyi Zhang, Amirhosein Javadi, Pengtao Xie
|
https://proceedings.mlr.press/v235/zhang24ai.html
|
ICML 2024
|
The Segment Anything Model (SAM), a foundation model pretrained on millions of images and segmentation masks, has significantly advanced semantic segmentation, a fundamental task in computer vision. Despite its strengths, SAM encounters two major challenges. Firstly, it struggles with segmenting specific objects autonomously, as it relies on users to manually input prompts like points or bounding boxes to identify targeted objects. Secondly, SAM faces challenges in excelling at specific downstream tasks, like medical imaging, due to a disparity between the distribution of its pretraining data, which predominantly consists of general-domain images, and the data used in downstream tasks. Current solutions to these problems, which involve finetuning SAM, often lead to overfitting, a notable issue in scenarios with very limited data, like in medical imaging. To overcome these limitations, we introduce BLO-SAM, which finetunes SAM based on bi-level optimization (BLO). Our approach allows for automatic image segmentation without the need for manual prompts, by optimizing a learnable prompt embedding. Furthermore, it significantly reduces the risk of overfitting by training the model’s weight parameters and the prompt embedding on two separate subsets of the training dataset, each at a different level of optimization. We apply BLO-SAM to diverse semantic segmentation tasks in general and medical domains. The results demonstrate BLO-SAM’s superior performance over various state-of-the-art image semantic segmentation methods. The code of BLO-SAM is available at https://github.com/importZL/BLO-SAM.
|
https://proceedings.mlr.press/v235/zhang24aj.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24aj/zhang24aj.pdf
|
https://openreview.net/forum?id=u00dmbI8Db
|
Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering
|
https://proceedings.mlr.press/v235/zhang24aj.html
|
Haoyu Zhang, Meng Liu, Zixin Liu, Xuemeng Song, Yaowei Wang, Liqiang Nie
|
https://proceedings.mlr.press/v235/zhang24aj.html
|
ICML 2024
|
The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. MFAS integrates a patch partition and merging module for enhanced small object recognition, a prior-guided patch selection module for noise suppression and focused analysis, and a hierarchical aggregation network to aggregate visual semantics guided by questions. Extensive experiments on several public egocentric datasets have validated the effectiveness and generalization of our framework. Code and data are available in https://github.com/Hyu-Zhang/EgoVideoQA.
|
https://proceedings.mlr.press/v235/zhang24ak.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ak/zhang24ak.pdf
|
https://openreview.net/forum?id=Vw4Yar2fmW
|
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
|
https://proceedings.mlr.press/v235/zhang24ak.html
|
He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu
|
https://proceedings.mlr.press/v235/zhang24ak.html
|
ICML 2024
|
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. In this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which we propose self-consistency training, an exact training method that does not require labeled data. It distinguishes the task from predicting other molecular properties by the following benefits: (1) it enables the model to be trained on a large amount of unlabeled data, hence addresses the data scarcity challenge and enhances generalization; (2) it is more efficient than running DFT to generate labels for supervised training, since it amortizes DFT calculation over a set of queries. We empirically demonstrate the better generalization in data-scarce and out-of-distribution scenarios, and the better efficiency over DFT labeling. These benefits push forward the applicability of Hamiltonian prediction to an ever-larger scale.
|
https://proceedings.mlr.press/v235/zhang24al.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24al/zhang24al.pdf
|
https://openreview.net/forum?id=aR3uxWlZhX
|
UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
|
https://proceedings.mlr.press/v235/zhang24al.html
|
Yunhao Zhang, Minghao Liu, Shengyang Zhou, Junchi Yan
|
https://proceedings.mlr.press/v235/zhang24al.html
|
ICML 2024
|
Despite the success of self-supervised pre-training in texts and images, applying it to multivariate time series (MTS) falls behind tailored methods for tasks like forecasting, imputation and anomaly detection. We propose a general-purpose framework, named UP2ME (Univariate Pre-training to Multivariate Fine-tuning). It conducts task-agnostic pre-training when downstream tasks are unspecified. Once the task and setting (e.g. forecasting length) are determined, it gives sensible solutions with frozen pre-trained parameters, which has not been achieved before. UP2ME is further refined by fine-tuning. A univariate-to-multivariate paradigm is devised to address the heterogeneity of temporal and cross-channel dependencies. In univariate pre-training, univariate instances with diverse lengths are generated for Masked AutoEncoder (MAE) pre-training, discarding cross-channel dependency. The pre-trained model handles downstream tasks by formulating them into specific mask-reconstruction problems. In multivariate fine-tuning, it constructs a dependency graph among channels using the pre-trained encoder to enhance cross-channel dependency capture. Experiments on eight real-world datasets show its SOTA performance in forecasting and imputation, approaching task-specific performance in anomaly detection. Our code is available at https://github.com/Thinklab-SJTU/UP2ME.
|
https://proceedings.mlr.press/v235/zhang24am.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24am/zhang24am.pdf
|
https://openreview.net/forum?id=KsUddQl39v
|
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
|
https://proceedings.mlr.press/v235/zhang24am.html
|
Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu
|
https://proceedings.mlr.press/v235/zhang24am.html
|
ICML 2024
|
Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations are constrained by a predefined perturbation budget $\epsilon$ and are equally applied to each pixel within an image. However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i.e., robustness) and accuracy on natural images (i.e., accuracy). Motivated by this finding, we propose Pixel-reweighted AdveRsarial Training (PART), a new framework that partially reduces $\epsilon$ for less influential pixels, guiding the model to focus more on key regions that affect its outputs. Specifically, we first use class activation mapping (CAM) methods to identify important pixel regions, then we keep the perturbation budget for these regions while lowering it for the remaining regions when generating AEs. In the end, we use these pixel-reweighted AEs to train a model. PART achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN and TinyImagenet-200, justifying the necessity to allocate distinct weights to different pixel regions in robust classification.
|
https://proceedings.mlr.press/v235/zhang24an.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24an/zhang24an.pdf
|
https://openreview.net/forum?id=Zc22RDtsvP
|
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
|
https://proceedings.mlr.press/v235/zhang24an.html
|
Kai Zhang, Yi Luan, Hexiang Hu, Kenton Lee, Siyuan Qiao, Wenhu Chen, Yu Su, Ming-Wei Chang
|
https://proceedings.mlr.press/v235/zhang24an.html
|
ICML 2024
|
Image retrieval, i.e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-based measures. Recent works leverage text instructions to allow users to more freely express their search intents. However, they primarily focus on image pairs that are visually similar and/or can be characterized by a small set of pre-defined relations. The core thesis of this paper is that text instructions can enable retrieving images with richer relations beyond visual similarity. To show this, we introduce MagicLens, a series of self-supervised image retrieval models that support open-ended instructions. MagicLens is built on a key novel insight: image pairs that naturally occur on the same web pages contain a wide range of implicit relations (e.g., inside view of), and we can bring those implicit relations explicit by synthesizing instructions via foundation models. Trained on 36.7M (query image, instruction, target image) triplets with rich semantic relations mined from the web, MagicLens achieves results comparable with or better than prior best on eight benchmarks of various image retrieval tasks, while maintaining high parameter efficiency with a significantly smaller model size. Additional human analyses on a 1.4M-image unseen corpus further demonstrate the diversity of search intents supported by MagicLens. Code and models are publicly available at the https://open-vision-language.github.io/MagicLens/.
|
https://proceedings.mlr.press/v235/zhang24ao.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ao/zhang24ao.pdf
|
https://openreview.net/forum?id=8iUgr2nuwo
|
Wukong: Towards a Scaling Law for Large-Scale Recommendation
|
https://proceedings.mlr.press/v235/zhang24ao.html
|
Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen
|
https://proceedings.mlr.press/v235/zhang24ao.html
|
ICML 2024
|
Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies of their upscaling mechanisms. This limitation poses significant challenges in adapting these models to increasingly more complex real-world datasets. In this paper, we propose an effective network architecture based purely on stacked factorization machines, and a synergistic upscaling strategy, collectively dubbed Wukong, to establish a scaling law in the domain of recommendation. Wukong’s unique design makes it possible to capture diverse, any-order of interactions simply through taller and wider layers. We conducted extensive evaluations on six public datasets, and our results demonstrate that Wukong consistently outperforms state-of-the-art models quality-wise. Further, we assessed Wukong’s scalability on an internal, large-scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 GFLOP/example, where prior arts fall short.
|
https://proceedings.mlr.press/v235/zhang24ap.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ap/zhang24ap.pdf
|
https://openreview.net/forum?id=1PMkV6oKw3
|
Nonparametric Teaching of Implicit Neural Representations
|
https://proceedings.mlr.press/v235/zhang24ap.html
|
Chen Zhang, Steven Tin Sui Luo, Jason Chun Lok Li, Yik Chung Wu, Ngai Wong
|
https://proceedings.mlr.press/v235/zhang24ap.html
|
ICML 2024
|
We investigate the learning of implicit neural representation (INR) using an overparameterized multilayer perceptron (MLP) via a novel nonparametric teaching perspective. The latter offers an efficient example selection framework for teaching nonparametrically defined (viz. non-closed-form) target functions, such as image functions defined by 2D grids of pixels. To address the costly training of INRs, we propose a paradigm called Implicit Neural Teaching (INT) that treats INR learning as a nonparametric teaching problem, where the given signal being fitted serves as the target function. The teacher then selects signal fragments for iterative training of the MLP to achieve fast convergence. By establishing a connection between MLP evolution through parameter-based gradient descent and that of function evolution through functional gradient descent in nonparametric teaching, we show for the first time that teaching an overparameterized MLP is consistent with teaching a nonparametric learner. This new discovery readily permits a convenient drop-in of nonparametric teaching algorithms to broadly enhance INR training efficiency, demonstrating 30%+ training time savings across various input modalities.
|
https://proceedings.mlr.press/v235/zhang24aq.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24aq/zhang24aq.pdf
|
https://openreview.net/forum?id=InUUQkExsw
|
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
|
https://proceedings.mlr.press/v235/zhang24aq.html
|
Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang
|
https://proceedings.mlr.press/v235/zhang24aq.html
|
ICML 2024
|
We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to enhance decision-making in scenarios where it is essential to manage uncertainty and minimize potential adverse outcomes. Particularly, our work focuses on applying the entropic risk measure to RL problems. While existing literature primarily investigates the online setting, there remains a large gap in understanding how to efficiently derive a near-optimal policy based on this risk measure using only a pre-collected dataset. We center on the linear Markov Decision Process (MDP) setting, a well-regarded theoretical framework that has yet to be examined from a risk-sensitive standpoint. In response, we introduce two provably sample-efficient algorithms. We begin by presenting a risk-sensitive pessimistic value iteration algorithm, offering a tight analysis by leveraging the structure of the risk-sensitive performance measure. To further improve the obtained bounds, we propose another pessimistic algorithm that utilizes variance information and reference-advantage decomposition, effectively improving both the dependence on the space dimension $d$ and the risk-sensitivity factor. To the best of our knowledge, we obtain the first provably efficient risk-sensitive offline RL algorithms.
|
https://proceedings.mlr.press/v235/zhang24ar.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ar/zhang24ar.pdf
|
https://openreview.net/forum?id=q0vILV7zAw
|
Sparse-to-dense Multimodal Image Registration via Multi-Task Learning
|
https://proceedings.mlr.press/v235/zhang24ar.html
|
Kaining Zhang, Jiayi Ma
|
https://proceedings.mlr.press/v235/zhang24ar.html
|
ICML 2024
|
Aligning image pairs captured by different sensors or those undergoing significant appearance changes is crucial for various computer vision and robotics applications. Existing approaches cope with this problem via either Sparse feature Matching (SM) or Dense direct Alignment (DA) paradigms. Sparse methods are efficient but lack accuracy in textureless scenes, while dense ones are more accurate in all scenes but demand for good initialization. In this paper, we propose SDME, a Sparse-to-Dense Multimodal feature Extractor based on a novel multi-task network that simultaneously predicts SM and DA features for robust multimodal image registration. We propose the sparse-to-dense registration paradigm: we first perform initial registration via SM and then refine the result via DA. By using the well-designed SDME, the sparse-to-dense approach combines the merits from both SM and DA. Extensive experiments on MSCOCO, GoogleEarth, VIS-NIR and VIS-IR-drone datasets demonstrate that our method achieves remarkable performance on multimodal cases. Furthermore, our approach exhibits robust generalization capabilities, enabling the fine-tuning of models initially trained on single-modal datasets for use with smaller multimodal datasets. Our code is available at https://github.com/KN-Zhang/SDME.
|
https://proceedings.mlr.press/v235/zhang24as.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24as/zhang24as.pdf
|
https://openreview.net/forum?id=f8G2KSCSdp
|
Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models
|
https://proceedings.mlr.press/v235/zhang24as.html
|
Jie Zhang, Xiaosong Ma, Song Guo, Peng Li, Wenchao Xu, Xueyang Tang, Zicong Hong
|
https://proceedings.mlr.press/v235/zhang24as.html
|
ICML 2024
|
Fine-tuning the learnable prompt for a pre-trained vision-language model (VLM), such as CLIP, has demonstrated exceptional efficiency in adapting to a broad range of downstream tasks. Existing prompt tuning methods for VLMs do not distinguish spurious features introduced by biased training data from invariant features, and employ a uniform alignment process when adapting to unseen target domains. This can impair the cross-modal feature alignment when the testing data significantly deviate from the distribution of the training data, resulting in a poor out-of-distribution (OOD) generalization performance. In this paper, we reveal that the prompt tuning failure in such OOD scenarios can be attribute to the undesired alignment between the textual and the spurious feature. As a solution, we propose CoOPood, a fine-grained prompt tuning method that can discern the causal features and deliberately align the text modality with the invariant feature. Specifically, we design two independent contrastive phases using two lightweight projection layers during the alignment, each with different objectives: 1) pulling the text embedding closer to invariant image embedding and 2) pushing text embedding away from spurious image embedding. We have illustrated that CoOPood can serve as a general framework for VLMs and can be seamlessly integrated with existing prompt tuning methods. Extensive experiments on various OOD datasets demonstrate the performance superiority over state-of-the-art methods.
|
https://proceedings.mlr.press/v235/zhang24at.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24at/zhang24at.pdf
|
https://openreview.net/forum?id=PAPY0cAB3C
|
In-Context Principle Learning from Mistakes
|
https://proceedings.mlr.press/v235/zhang24at.html
|
Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, Uri Alon
|
https://proceedings.mlr.press/v235/zhang24at.html
|
ICML 2024
|
In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this paradigm, by learning more from the few given input-output examples. We introduce Learning Principles (LEAP): First, we intentionally induce the model to make mistakes on these few examples; then we reflect on these mistakes, and learn explicit task-specific “principles” from them, which help solve similar problems and avoid common mistakes; finally, we prompt the model to answer unseen test questions using the original few-shot examples and these learned general principles. We evaluate LEAP on a wide range of benchmarks, including multi-hop question answering (Hotpot QA), textual QA (DROP), Big-Bench Hard reasoning, and math problems (GSM8K and MATH); in all these benchmarks, LEAP improves the strongest available LLMs such as GPT-3.5-turbo, GPT-4, GPT-4-turbo and Claude-2.1. For example, LEAP improves over the standard few-shot prompting using GPT-4 by 7.5% in DROP, and by 3.3% in HotpotQA. Importantly, LEAP does not require any more input or examples than the standard few-shot prompting settings.
|
https://proceedings.mlr.press/v235/zhang24au.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24au/zhang24au.pdf
|
https://openreview.net/forum?id=M3qRRkOuTN
|
Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach
|
https://proceedings.mlr.press/v235/zhang24au.html
|
Bin Zhang, Hangyu Mao, Lijuan Li, Zhiwei Xu, Dapeng Li, Rui Zhao, Guoliang Fan
|
https://proceedings.mlr.press/v235/zhang24au.html
|
ICML 2024
|
Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely restricted by network architectures or environmental settings. To address this issue, we propose the Stackelberg Decision Transformer (STEER). It efficiently manages decision-making processes by incorporating the hierarchical decision structure of SG, the modeling capability of autoregressive sequence models, and the exploratory learning methodology of MARL. Our approach exhibits broad applicability across diverse task types and environmental configurations in MAS. Experimental results demonstrate both the convergence of our method towards Stackelberg equilibrium strategies and its superiority over strong baselines in complex scenarios.
|
https://proceedings.mlr.press/v235/zhang24av.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24av/zhang24av.pdf
|
https://openreview.net/forum?id=DRGgT7SyC7
|
Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities
|
https://proceedings.mlr.press/v235/zhang24av.html
|
Stephen Zhang, Vardan Papyan
|
https://proceedings.mlr.press/v235/zhang24av.html
|
ICML 2024
|
Pruning has emerged as a promising approach for compressing large-scale models, yet its effectiveness in recovering the sparsest of models has not yet been explored. We conducted an extensive series of 485,838 experiments, applying a range of state-of-the-art pruning algorithms to a synthetic dataset we created, named the Cubist Spiral. Our findings reveal a significant gap in performance compared to ideal sparse networks, which we identified through a novel combinatorial search algorithm. We attribute this performance gap to current pruning algorithms’ poor behaviour under overparameterization, their tendency to induce disconnected paths throughout the network, and their propensity to get stuck at suboptimal solutions, even when given the optimal width and initialization. This gap is concerning, given the simplicity of the network architectures and datasets used in our study. We hope that our research encourages further investigation into new pruning techniques that strive for true network sparsity.
|
https://proceedings.mlr.press/v235/zhang24aw.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24aw/zhang24aw.pdf
|
https://openreview.net/forum?id=NkN6wrYXe5
|
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization
|
https://proceedings.mlr.press/v235/zhang24aw.html
|
Xinwen Zhang, Ali Payani, Myungjin Lee, Richard Souvenir, Hongchang Gao
|
https://proceedings.mlr.press/v235/zhang24aw.html
|
ICML 2024
|
AUC maximization is an effective approach to address the imbalanced data classification problem in federated learning. In the past few years, a couple of federated AUC maximization approaches have been developed based on the minimax optimization. However, directly solving a minimax optimization problem to maximize the AUC score cannot achieve satisfactory performance. To address this issue, we propose to maximize AUC via optimizing a federated multi-level compositional minimax problem. Specifically, we develop a novel federated multi-level compositional minimax algorithm with rigorous theoretical guarantees to solve this new learning paradigm in both algorithmic design and theoretical analysis. To the best of our knowledge, this is the first work studying the multi-level minimax optimization problem. Additionally, extensive empirical evaluations confirm the efficacy of our proposed approach.
|
https://proceedings.mlr.press/v235/zhang24ax.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ax/zhang24ax.pdf
|
https://openreview.net/forum?id=IwqE4QqBew
|
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
|
https://proceedings.mlr.press/v235/zhang24ax.html
|
Fangzhao Zhang, Mert Pilanci
|
https://proceedings.mlr.press/v235/zhang24ax.html
|
ICML 2024
|
Low-Rank Adaptation (LoRA) emerges as a popular parameter-efficient fine-tuning (PEFT) method, which proposes to freeze pretrained model weights and update an additive low-rank trainable matrix. In this work, we study the enhancement of LoRA training by introducing an $r\times r$ preconditioner in each gradient step where $r$ is the LoRA rank. We theoretically verify that the proposed preconditioner stabilizes feature learning with LoRA under infinite-width NN setting. Empirically, the implementation of this new preconditioner requires a small change to existing optimizer code and creates virtually minuscule storage and runtime overhead. Our experimental results with both large language models and text-to-image diffusion models show that with this new preconditioner, the convergence and reliability of SGD and AdamW can be significantly enhanced. Moreover, the training process becomes much more robust to hyperparameter choices such as learning rate. The new preconditioner can be derived from a novel Riemannian metric in low-rank matrix field.
|
https://proceedings.mlr.press/v235/zhang24ay.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ay/zhang24ay.pdf
|
https://openreview.net/forum?id=FPlaQyAGHu
|
How Language Model Hallucinations Can Snowball
|
https://proceedings.mlr.press/v235/zhang24ay.html
|
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah A. Smith
|
https://proceedings.mlr.press/v235/zhang24ay.html
|
ICML 2024
|
A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we show that LMs sometimes produce hallucinations that they can separately recognize as incorrect. To do this, we construct three question-answering datasets where LMs often state an incorrect answer which is followed by an explanation with at least one incorrect claim. Crucially, we find that GPT-3.5, GPT-4, and LLaMA2-70B-chat can identify 67%, 87%, and 94% of these incorrect claims, respectively. We show that this phenomenon doesn’t disappear under higher temperatures sampling, beam search, and zero-shot chain-of-thought prompting. These findings reveal that LM hallucinations can snowball: early mistakes by an LM can lead to more mistakes that otherwise would not be made.
|
https://proceedings.mlr.press/v235/zhang24az.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24az/zhang24az.pdf
|
https://openreview.net/forum?id=QgvBcOsF4B
|
Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models
|
https://proceedings.mlr.press/v235/zhang24az.html
|
Zixin Zhang, Fan Qi, Changsheng Xu
|
https://proceedings.mlr.press/v235/zhang24az.html
|
ICML 2024
|
The remarkable generalization of large-scale models has recently gained significant attention in multimodal research. However, deploying heterogeneous large-scale models with different modalities under Federated Learning (FL) to protect data privacy imposes tremendous challenges on clients’ limited computation and storage. In this work, we propose M$^2$FedSA to address the above issue. We realize modularized decomposition of large-scale models via Split Learning (SL) and only retain privacy-sensitive modules on clients, alleviating storage overhead. By freezing large-scale models and introducing two specialized lightweight adapters, the models can better focus on task-specific knowledge and enhance modality-specific knowledge, improving the model’s adaptability to different tasks while balancing efficiency. In addition, M$^2$FedSA further improves performance by transferring multimodal knowledge to unimodal clients at both the feature and decision levels, which leverages the complementarity of different modalities. Extensive experiments on various multimodal classification tasks validate the effectiveness of our proposed M$^2$FedSA. The code is made available publicly at https://github.com/M2FedSA/M-2FedSA.
|
https://proceedings.mlr.press/v235/zhang24ba.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ba/zhang24ba.pdf
|
https://openreview.net/forum?id=RsIMGYzBcv
|
Online Resource Allocation with Non-Stationary Customers
|
https://proceedings.mlr.press/v235/zhang24ba.html
|
Xiaoyue Zhang, Hanzhang Qin, Mabel Chou
|
https://proceedings.mlr.press/v235/zhang24ba.html
|
ICML 2024
|
We propose a novel algorithm for online resource allocation with non-stationary customer arrivals and unknown click-through rates. We assume multiple types of customers arriving in a nonstationary stochastic fashion, with unknown arrival rates in each period. Additionally, customers’ click-through rates are assumed to be unknown and only learnable online. By leveraging results from the stochastic contextual bandit with knapsack and online matching with adversarial arrivals, we develop an online scheme to allocate the resources to nonstationary customers. We prove that under mild conditions, our scheme achieves a “best-of-both-world” result: the scheme has a sublinear regret when the customer arrivals are near-stationary, and enjoys an optimal competitive ratio under general (non-stationary) customer arrival distributions. Finally, we conduct extensive numerical experiments to show our approach generates near-optimal revenues for all different customer scenarios.
|
https://proceedings.mlr.press/v235/zhang24bb.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bb/zhang24bb.pdf
|
https://openreview.net/forum?id=zji9DLksTz
|
Flexible Residual Binarization for Image Super-Resolution
|
https://proceedings.mlr.press/v235/zhang24bb.html
|
Yulun Zhang, Haotong Qin, Zixiang Zhao, Xianglong Liu, Martin Danelljan, Fisher Yu
|
https://proceedings.mlr.press/v235/zhang24bb.html
|
ICML 2024
|
Binarized image super-resolution (SR) has attracted much research attention due to its potential to drastically reduce parameters and operations. However, most binary SR works binarize network weights directly, which hinders high-frequency information extraction. Furthermore, as a pixel-wise reconstruction task, binarization often results in heavy representation content distortion. To address these issues, we propose a flexible residual binarization (FRB) method for image SR. We first propose a second-order residual binarization (SRB), to counter the information loss caused by binarization. In addition to the primary weight binarization, we also binarize the reconstruction error, which is added as a residual term in the prediction. Furthermore, to narrow the representation content gap between the binarized and full-precision networks, we propose Distillation-guided Binarization Training (DBT). We uniformly align the contents of different bit widths by constructing a normalized attention form. Finally, we generalize our method by applying our FRB to binarize convolution and Transformer-based SR networks, resulting in two binary baselines: FRBC and FRBT. We conduct extensive experiments and comparisons with recent leading binarization methods. Our proposed baselines, FRBC and FRBT, achieve superior performance both quantitatively and visually. The code and model will be released.
|
https://proceedings.mlr.press/v235/zhang24bc.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bc/zhang24bc.pdf
|
https://openreview.net/forum?id=BrZPj9rEpN
|
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
|
https://proceedings.mlr.press/v235/zhang24bc.html
|
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang, Yang Yu
|
https://proceedings.mlr.press/v235/zhang24bc.html
|
ICML 2024
|
Developing policies that can adapt to non-stationary environments is essential for real-world reinforcement learning applications. Nevertheless, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because the limited offline data makes it hard for the context encoder to differentiate between changes in the environment dynamics and shifts in the behavior policy, often leading to context misassociations. To address this issue, we introduce a novel approach called debiased offline representation learning for fast online adaptation (DORA). DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy. We present a practical implementation of DORA, leveraging tractable bounds of the information bottleneck principle. Our experimental evaluation across six benchmark MuJoCo tasks with variable parameters demonstrates that DORA not only achieves a more precise dynamics encoding but also significantly outperforms existing baselines in terms of performance.
|
https://proceedings.mlr.press/v235/zhang24bd.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bd/zhang24bd.pdf
|
https://openreview.net/forum?id=O6tenHWTUU
|
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
|
https://proceedings.mlr.press/v235/zhang24bd.html
|
Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai
|
https://proceedings.mlr.press/v235/zhang24bd.html
|
ICML 2024
|
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior performance for algorithms that conflate observations with state. Partially Observable Markov Decision Processes (POMDPs), on the other hand, provide a general framework that allows for partial observability to be accounted for in learning, exploration and planning, but presents significant computational and statistical challenges. To address these difficulties, we develop a representation-based perspective that leads to a coherent framework and tractable algorithmic approach for practical reinforcement learning from partial observations. We provide a theoretical analysis for justifying the statistical efficiency of the proposed algorithm, and also empirically demonstrate the proposed algorithm can surpass state-of-the-art performance with partial observations across various benchmarks, advancing reliable reinforcement learning towards more practical applications.
|
https://proceedings.mlr.press/v235/zhang24be.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24be/zhang24be.pdf
|
https://openreview.net/forum?id=6KtXzUUEp4
|
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks
|
https://proceedings.mlr.press/v235/zhang24be.html
|
Lujing Zhang, Aaron Roth, Linjun Zhang
|
https://proceedings.mlr.press/v235/zhang24be.html
|
ICML 2024
|
This paper introduces a framework for post-processing machine learning models so that their predictions satisfy multi-group fairness guarantees. Based on the celebrated notion of multicalibration, we introduce $(s,g,\alpha)-$GMC (Generalized Multi-Dimensional Multicalibration) for multi-dimensional mappings $s$, constraints $g$, and a pre-specified threshold level $\alpha$. We propose associated algorithms to achieve this notion in general settings. This framework is then applied to diverse scenarios encompassing different fairness concerns, including false negative rate control in image segmentation, prediction set conditional uncertainty quantification in hierarchical classification, and de-biased text generation in language models. We conduct numerical studies on several datasets and tasks.
|
https://proceedings.mlr.press/v235/zhang24bf.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bf/zhang24bf.pdf
|
https://openreview.net/forum?id=TujtZgdRxB
|
Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning
|
https://proceedings.mlr.press/v235/zhang24bf.html
|
Qiankun Zhang, Aocheng Shen, Boyu Zhang, Hanrui Jiang, Bingqian Du
|
https://proceedings.mlr.press/v235/zhang24bf.html
|
ICML 2024
|
For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is found. One is to continuously design algorithms with better performance. To this end, reinforcement learning (RL) has demonstrated great success in literature. However, little is known on the other direction: whether RL helps explore how hard an online problem is. In this paper, we study a generalized model of OBM, named online matching with stochastic rewards (OMSR, FOCS 2012), for which the optimal competitive ratio is still unknown. We adopt an adversarial RL approach that trains two RL agents adversarially and iteratively: the algorithm agent learns for algorithms with larger competitive ratios, while the adversarial agent learns to produce a family of hard instances. Through such a framework, agents converge at the end with a robust algorithm, which empirically outperforms the state of the art (STOC 2020). Much more significantly, it allows to track how the hard instances are generated. We succeed in distilling two structural properties from the learned graph patterns, which remarkably reduce the action space, and further enable theoretical improvement on the best-known hardness result of OMSR, from $0.621$ (FOCS 2012) to $0.597$. To the best of our knowledge, this gives the first evidence that RL can help enhance the theoretical understanding of an online problem.
|
https://proceedings.mlr.press/v235/zhang24bg.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bg/zhang24bg.pdf
|
https://openreview.net/forum?id=XwnABAdH5y
|
Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning
|
https://proceedings.mlr.press/v235/zhang24bg.html
|
Zongmeng Zhang, Yufeng Shi, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li
|
https://proceedings.mlr.press/v235/zhang24bg.html
|
ICML 2024
|
Trustworthiness is an essential prerequisite for the real-world application of large language models. In this paper, we focus on the trustworthiness of language models with respect to retrieval augmentation. Despite being supported with external evidence, retrieval-augmented generation still suffers from hallucinations, one primary cause of which is the conflict between contextual and parametric knowledge. We deem that retrieval-augmented language models have the inherent capabilities of supplying response according to both contextual and parametric knowledge. Inspired by aligning language models with human preference, we take the first step towards aligning retrieval-augmented language models to a status where it responds relying merely on the external evidence and disregards the interference of parametric knowledge. Specifically, we propose a reinforcement learning based algorithm Trustworthy-Alignment, theoretically and experimentally demonstrating large language models’ capability of reaching a trustworthy status without explicit supervision on how to respond. Our work highlights the potential of large language models on exploring its intrinsic abilities by its own and expands the application scenarios of alignment from fulfilling human preference to creating trustworthy agents.
|
https://proceedings.mlr.press/v235/zhang24bh.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bh/zhang24bh.pdf
|
https://openreview.net/forum?id=fOBas5H4Xc
|
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
|
https://proceedings.mlr.press/v235/zhang24bh.html
|
Yuyang Zhang, Shahriar Talebi, Na Li
|
https://proceedings.mlr.press/v235/zhang24bh.html
|
ICML 2024
|
In this paper, we focus on learning a linear time-invariant (LTI) model with low-dimensional latent variables but high-dimensional observations. We provide an algorithm that recovers the high-dimensional features, i.e. column space of the observer, embeds the data into low dimensions and learns the low-dimensional model parameters. Our algorithm enjoys a sample complexity guarantee of order $\tilde{\mathcal{O}}(n/\epsilon^2)$, where $n$ is the observation dimension. We further establish a fundamental lower bound indicating this complexity bound is optimal up to logarithmic factors and dimension-independent constants. We show that this inevitable linear factor of $n$ is due to the learning error of the observer’s column space in the presence of high-dimensional noises. Extending our results, we consider a meta-learning problem inspired by various real-world applications, where the observer column space can be collectively learned from datasets of multiple LTI systems. An end-to-end algorithm is then proposed, facilitating learning LTI systems from a meta-dataset which breaks the sample complexity lower bound in certain scenarios.
|
https://proceedings.mlr.press/v235/zhang24bi.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bi/zhang24bi.pdf
|
https://openreview.net/forum?id=wleAlsklEh
|
Matrix Information Theory for Self-Supervised Learning
|
https://proceedings.mlr.press/v235/zhang24bi.html
|
Yifan Zhang, Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan
|
https://proceedings.mlr.press/v235/zhang24bi.html
|
ICML 2024
|
The maximum entropy encoding framework provides a unified perspective for many non-contrastive learning methods like SimSiam, Barlow Twins, and MEC. Inspired by this framework, we introduce Matrix-SSL, a novel approach that leverages matrix information theory to interpret the maximum entropy encoding loss as matrix uniformity loss. Furthermore, Matrix-SSL enhances the maximum entropy encoding method by seamlessly incorporating matrix alignment loss, directly aligning covariance matrices in different branches. Experimental results reveal that Matrix-SSL outperforms state-of-the-art methods on the ImageNet dataset under linear evaluation settings and on MS-COCO for transfer learning tasks. Specifically, when performing transfer learning tasks on MS-COCO, our method outperforms previous SOTA methods such as MoCo v2 and BYOL up to 3.3% with only 400 epochs compared to 800 epochs pre-training. We also try to introduce representation learning into the language modeling regime by fine-tuning a 7B model using matrix cross-entropy loss, with a margin of 3.1% on the GSM8K dataset over the standard cross-entropy loss.
|
https://proceedings.mlr.press/v235/zhang24bj.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bj/zhang24bj.pdf
|
https://openreview.net/forum?id=LGhtl9ktop
|
Switchable Decision: Dynamic Neural Generation Networks
|
https://proceedings.mlr.press/v235/zhang24bj.html
|
Shujian Zhang, Korawat Tanwisuth, Chengyue Gong, Pengcheng He, Mingyuan Zhou
|
https://proceedings.mlr.press/v235/zhang24bj.html
|
ICML 2024
|
Auto-regressive generation models achieve competitive performance across many different NLP tasks such as summarization, question answering, and classifications. However, they are also known for being slow in inference, which makes them challenging to deploy in real-time applications. We propose a switchable decision to accelerate inference by dynamically assigning computation resources for each data instance. Automatically making decisions on where to skip and how to balance quality and computation cost with constrained optimization, our dynamic neural generation networks enforce the efficient inference path and determine the optimized trade-off. Experiments across question answering, summarization, and classification benchmarks show that our method benefits from less computation cost during inference while keeping the same accuracy. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many NLP tasks.
|
https://proceedings.mlr.press/v235/zhang24bk.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bk/zhang24bk.pdf
|
https://openreview.net/forum?id=CfOtiepP8s
|
Interpreting and Improving Large Language Models in Arithmetic Calculation
|
https://proceedings.mlr.press/v235/zhang24bk.html
|
Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-Ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye
|
https://proceedings.mlr.press/v235/zhang24bk.html
|
ICML 2024
|
Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathematical computations. However, even for the simplest arithmetic calculations, the intrinsic mechanisms behind LLMs remains mysterious, making it challenging to ensure reliability. In this work, we delve into uncovering a specific mechanism by which LLMs execute calculations. Through comprehensive experiments, we find that LLMs frequently involve a small fraction ($<$5%) of attention heads, which play a pivotal role in focusing on operands and operators during calculation processes. Subsequently, the information from these operands is processed through multi-layer perceptrons (MLPs), progressively leading to the final solution. These pivotal heads/MLPs, though identified on a specific dataset, exhibit transferability across different datasets and even distinct tasks. This insight prompted us to investigate the potential benefits of selectively fine-tuning these essential heads/MLPs to boost the LLMs’ computational performance. We empirically find that such precise tuning can yield notable enhancements on mathematical prowess, without compromising the performance on non-mathematical tasks. Our work serves as a preliminary exploration into the arithmetic calculation abilities inherent in LLMs, laying a solid foundation to reveal more intricate mathematical tasks.
|
https://proceedings.mlr.press/v235/zhang24bl.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bl/zhang24bl.pdf
|
https://openreview.net/forum?id=k5ncz7TIPX
|
Directly Denoising Diffusion Models
|
https://proceedings.mlr.press/v235/zhang24bl.html
|
Dan Zhang, Jingjing Wang, Feng Luo
|
https://proceedings.mlr.press/v235/zhang24bl.html
|
ICML 2024
|
In this paper, we present Directly Denoising Diffusion Models (DDDMs): a simple and generic approach for generating realistic images with few-step sampling, while multistep sampling is still preserved for better performance. DDDMs require no delicately designed samplers nor distillation on pre-trained distillation models. DDDMs train the diffusion model conditioned on an estimated target that was generated from previous training iterations of its own. To generate images, samples generated from previous timestep are also taken into consideration, guiding the generation process iteratively. We further propose Pseudo-LPIPS, a novel metric loss that is more robust to various values of hyperparameter. Despite its simplicity, the proposed approach can achieve strong performance in benchmark datasets. Our model achieves FID scores of 2.57 and 2.33 on CIFAR-10 in one-step and two-step sampling respectively, surpassing those obtained from GANs and distillation-based models. By extending the sampling to 1000 steps, we further reduce FID score to 1.79, aligning with state-of-the-art methods in the literature. For ImageNet 64x64, our approach stands as a competitive contender against leading models.
|
https://proceedings.mlr.press/v235/zhang24bm.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bm/zhang24bm.pdf
|
https://openreview.net/forum?id=Hg7C5YYifi
|
Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet
|
https://proceedings.mlr.press/v235/zhang24bm.html
|
Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, Wenwu Zhu
|
https://proceedings.mlr.press/v235/zhang24bm.html
|
ICML 2024
|
The existing graph neural architecture search (GNAS) methods assume that the graph tasks are static during the search process, ignoring the ubiquitous scenarios where sequential graph tasks come in a continual fashion. Moreover, existing GNAS works resort to entangled graph factors during the architecture search process, resulting in the catastrophic forgetting problems. In this paper, we study the problem of continual graph neural architecture search that is expected to continually search the architecture to learn new graph tasks without forgetting the past, which remains largely unexplored in the literature. However, this problem poses the challenge of architecture conflicts, i.e., the optimal architecture for the new graph task may have performance deterioration and thus sub-optimal for past tasks. To address the challenge, we propose a novel Disentangled Continual Graph Neural Architecture Search with Invariant Modularization (GASIM) method, which is able to continually search the optimal architectures without forgetting past knowledge. Specifically, we first design a modular graph architecture super-network incorporating multiple modules to enable searching architecture with factor expertise. Second, we propose a factor-based task-module router that discovers the latent graph factors and routes the incoming task to the best suitable architecture module to alleviate the forgetting problem induced by architecture conflicts. Finally, we propose an invariant architecture search mechanism to capture the shared knowledge among tasks. Extensive experiments on real-world datasets demonstrate that the proposed method achieves state-of-the-art performance against baselines in continual graph neural architecture search.
|
https://proceedings.mlr.press/v235/zhang24bn.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bn/zhang24bn.pdf
|
https://openreview.net/forum?id=4mU6LNMaIu
|
GroupCover: A Secure, Efficient and Scalable Inference Framework for On-device Model Protection based on TEEs
|
https://proceedings.mlr.press/v235/zhang24bn.html
|
Zheng Zhang, Na Wang, Ziqi Zhang, Yao Zhang, Tianyi Zhang, Jianwei Liu, Ye Wu
|
https://proceedings.mlr.press/v235/zhang24bn.html
|
ICML 2024
|
Due to the high cost of training DNN models, how to protect the intellectual property of DNN models, especially when the models are deployed to users’ devices, is becoming an important topic. One practical solution is to use Trusted Execution Environments (TEEs) and researchers have proposed various model obfuscation solutions to make full use of the high-security guarantee of TEEs and the high performance of collocated GPUs. In this paper, we first identify a common vulnerability, namely the fragility of randomness, that is shared by existing TEE-based model obfuscation solutions. This vulnerability benefits model-stealing attacks and allows the adversary to recover about 97% of the secret model. To improve the security of TEE-shielded DNN models, we further propose a new model obfuscation approach GroupCover, which uses sufficient randomization and mutual covering obfuscation to protect model weights. Experimental results demonstrate that GroupCover can achieve a comparable security level as the upper-bound (black-box protection), which is remarkably over 3x compared with existing solutions. Besides, GroupCover introduces 19% overhead and negligible accuracy loss compared to model unprotected scheme.
|
https://proceedings.mlr.press/v235/zhang24bo.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bo/zhang24bo.pdf
|
https://openreview.net/forum?id=sBJNokmYuV
|
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
|
https://proceedings.mlr.press/v235/zhang24bo.html
|
Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
|
https://proceedings.mlr.press/v235/zhang24bo.html
|
ICML 2024
|
Fine-tuning vision-language models (VLMs) with abundant unlabeled data recently has attracted increasing attention. Existing methods that resort to the pseudolabeling strategy would suffer from heavily incorrect hard pseudolabels when VLMs exhibit low zero-shot performance in downstream tasks. To alleviate this issue, we propose a Candidate Pseudolabel Learning method, termed CPL, to fine-tune VLMs with suitable candidate pseudolabels of unlabeled data in downstream tasks. The core of our method lies in the generation strategy of candidate pseudolabels, which progressively generates refined candidate pseudolabels by both intra- and inter-instance label selection, based on a confidence score matrix for all unlabeled data. This strategy can result in better performance in true label inclusion and class-balanced instance selection. In this way, we can directly apply existing loss functions to learn with generated candidate psueudolabels. Extensive experiments on nine benchmark datasets with three learning paradigms demonstrate the effectiveness of our method. Our code can be found here.
|
https://proceedings.mlr.press/v235/zhang24bp.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bp/zhang24bp.pdf
|
https://openreview.net/forum?id=1vGN3CSxVs
|
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
|
https://proceedings.mlr.press/v235/zhang24bp.html
|
Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang
|
https://proceedings.mlr.press/v235/zhang24bp.html
|
ICML 2024
|
Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized protein into a 3D CNN for prediction. However, the CNN-based methods encounter several critical issues: 1) defective in representing irregular protein structures; 2) sensitive to rotations; 3) insufficient to characterize the protein surface; 4) unaware of protein size shift. To address the above issues, this work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction, which comprises three modules: the first one to extract local geometric information for each surface atom, the second one to model both the chemical and spatial structure of protein and the last one to capture the geometry of the surface via equivariant message passing over the surface atoms. We further propose a dense attention output layer to alleviate the effect incurred by variable protein size. Extensive experiments on several representative benchmarks demonstrate the superiority of our framework to the state-of-the-art methods.
|
https://proceedings.mlr.press/v235/zhang24bq.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bq/zhang24bq.pdf
|
https://openreview.net/forum?id=KfXXPCcobh
|
Exploring the Benefit of Activation Sparsity in Pre-training
|
https://proceedings.mlr.press/v235/zhang24bq.html
|
Zhengyan Zhang, Chaojun Xiao, Qiujieli Qin, Yankai Lin, Zhiyuan Zeng, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou
|
https://proceedings.mlr.press/v235/zhang24bq.html
|
ICML 2024
|
Pre-trained Transformers inherently possess the characteristic of sparse activation, where only a small fraction of the neurons are activated for each token. While sparse activation has been explored through post-training methods, its potential in pre-training remains untapped. In this work, we first study how activation properties change during pre-training. Our examination reveals that Transformers exhibit sparse activation throughout the majority of the pre-training process while the activation correlation keeps evolving as training progresses. Leveraging this observation, we propose Switchable Sparse-Dense Learning (SSD). SSD adaptively switches between the Mixtures-of-Experts (MoE) based sparse training and the conventional dense training during the pre-training process, leveraging the efficiency of sparse training and avoiding the static activation correlation of sparse training. Compared to dense training, SSD achieves comparable performance with identical model size and reduces pre-training costs. Moreover, the models trained with SSD can be directly used as MoE models for sparse inference and achieve the same performance as dense models with up to $2\times$ faster inference speed. Codes are available at https://github.com/thunlp/moefication.
|
https://proceedings.mlr.press/v235/zhang24br.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24br/zhang24br.pdf
|
https://openreview.net/forum?id=Pte6iiXvpf
|
Causal Representation Learning from Multiple Distributions: A General Setting
|
https://proceedings.mlr.press/v235/zhang24br.html
|
Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
|
https://proceedings.mlr.press/v235/zhang24br.html
|
ICML 2024
|
In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the latent causal variables (e.g., the underlying concepts or objects). For the purpose of making predictions in changing environments or making proper changes to the system, it is helpful to recover the latent causal variables $Z_i$ and their causal relations represented by graph $\mathcal{G}_Z$. This problem has recently been known as causal representation learning. This paper is concerned with a general, completely nonparametric setting of causal representation learning from multiple distributions (arising from heterogeneous data or nonstationary time series), without assuming hard interventions behind distribution changes. We aim to develop general solutions in this fundamental case; as a by product, this helps see the unique benefit offered by other assumptions such as parametric causal models or hard interventions. We show that under the sparsity constraint on the recovered graph over the latent variables and suitable sufficient change conditions on the causal influences, interestingly, one can recover the moralized graph of the underlying directed acyclic graph, and the recovered latent variables and their relations are related to the underlying causal model in a specific, nontrivial way. In some cases, most latent variables can even be recovered up to component-wise transformations. Experimental results verify our theoretical claims.
|
https://proceedings.mlr.press/v235/zhang24bs.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bs/zhang24bs.pdf
|
https://openreview.net/forum?id=cVp8blEw2i
|
FESSNC: Fast Exponentially Stable and Safe Neural Controller
|
https://proceedings.mlr.press/v235/zhang24bs.html
|
Jingdong Zhang, Luan Yang, Qunxi Zhu, Wei Lin
|
https://proceedings.mlr.press/v235/zhang24bs.html
|
ICML 2024
|
In order to stabilize nonlinear systems modeled by stochastic differential equations, we design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning controllers. Our framework is parameterized by neural networks, and realizing both rigorous exponential stability and safety guarantees. Concretely, we design heuristic methods to learn the exponentially stable and the safe controllers, respectively, in light of the classical theory of stochastic exponential stability and our established theorem on guaranteeing the almost-sure safety for stochastic dynamics. More significantly, to rigorously ensure the stability and the safety guarantees for the learned controllers, we develop a projection operator, projecting to the space of exponentially-stable and safe controllers. To reduce the highly computational cost for solving the projection operation, approximate projection operators are delicately proposed with closed forms that map the learned controllers to the target controller space. Furthermore, we employ Hutchinson’s trace estimator for a scalable unbiased estimate of the Hessian matrix that is used in the projection operator, which thus allows for reducing computational cost and, therefore, can accelerate the training and testing processes. More importantly, our approximate projection operations are applicable to the nonparametric control methods, improving their stability and safety performance. We empirically demonstrate the superiority of the FESSNC over the existing methods.
|
https://proceedings.mlr.press/v235/zhang24bt.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bt/zhang24bt.pdf
|
https://openreview.net/forum?id=yoTCwNqQS6
|
Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective
|
https://proceedings.mlr.press/v235/zhang24bt.html
|
Yulong Zhang, Yuan Yao, Shuhao Chen, Pengrong Jin, Yu Zhang, Jian Jin, Jiangang Lu
|
https://proceedings.mlr.press/v235/zhang24bt.html
|
ICML 2024
|
Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://github.com/zhangyl660/LERM.
|
https://proceedings.mlr.press/v235/zhang24bu.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bu/zhang24bu.pdf
|
https://openreview.net/forum?id=ZAW37OZ6ig
|
NExT-Chat: An LMM for Chat, Detection and Segmentation
|
https://proceedings.mlr.press/v235/zhang24bu.html
|
Ao Zhang, Yuan Yao, Wei Ji, Zhiyuan Liu, Tat-Seng Chua
|
https://proceedings.mlr.press/v235/zhang24bu.html
|
ICML 2024
|
The development of large language models (LLMs) has greatly advanced the field of multimodal understanding, leading to the emergence of large multimodal models (LMMs). In order to enhance visual comprehension, recent studies have equipped LMMs with region-level understanding capabilities by representing object bounding box coordinates as a series of text sequences (pix2seq). In this paper, we introduce a novel paradigm for object location modeling called the pix2emb method, where we ask the LMM to output the location embeddings and then decode them with different decoders. This paradigm allows us to use different location formats (such as bounding boxes and masks) in multimodal conversations. Leveraging the proposed pix2emb method, we train an LMM named NExT-Chat and demonstrate its capability of handling multiple tasks like visual grounding, region captioning, and grounded reasoning. Comprehensive experiments show the effectiveness of our NExT-Chat on various tasks, e.g., NExT-Chat (87.7) vs. Shikra (86.9) on POPE-Random, NExT-Chat (71.3) vs. LISA (67.9) on referring expression segmentation task, and NExT-Chat (79.6) vs. Kosmos-2 (62.3) on region caption task.
|
https://proceedings.mlr.press/v235/zhang24bv.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bv/zhang24bv.pdf
|
https://openreview.net/forum?id=wTd7dogTsB
|
Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
|
https://proceedings.mlr.press/v235/zhang24bv.html
|
Kaihong Zhang, Heqi Yin, Feng Liang, Jingbo Liu
|
https://proceedings.mlr.press/v235/zhang24bv.html
|
ICML 2024
|
We study the asymptotic error of score-based diffusion model sampling in large-sample scenarios from a non-parametric statistics perspective. We show that a kernel-based score estimator achieves an optimal mean square error of $\widetilde{O}\left(n^{-1} t^{-\frac{d+2}{2}}(t^{\frac{d}{2}} \vee 1)\right)$ for the score function of $p_0*\mathcal{N}(0,t\boldsymbol{I}_d)$, where $n$ and $d$ represent the sample size and the dimension, $t$ is bounded above and below by polynomials of $n$, and $p_0$ is an arbitrary sub-Gaussian distribution. As a consequence, this yields an $\widetilde{O}\left(n^{-1/2} t^{-\frac{d}{4}}\right)$ upper bound for the total variation error of the distribution of the sample generated by the diffusion model under a mere sub-Gaussian assumption. If in addition, $p_0$ belongs to the nonparametric family of the $\beta$-Sobolev space with $\beta\le 2$, by adopting an early stopping strategy, we obtain that the diffusion model is nearly (up to log factors) minimax optimal. This removes the crucial lower bound assumption on $p_0$ in previous proofs of the minimax optimality of the diffusion model for nonparametric families.
|
https://proceedings.mlr.press/v235/zhang24bw.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bw/zhang24bw.pdf
|
https://openreview.net/forum?id=UZlMXUGI6e
|
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
|
https://proceedings.mlr.press/v235/zhang24bw.html
|
Weijia Zhang, Chenlong Yin, Hao Liu, Xiaofang Zhou, Hui Xiong
|
https://proceedings.mlr.press/v235/zhang24bw.html
|
ICML 2024
|
Forecasting of Irregular Multivariate Time Series (IMTS) is critical for numerous areas, such as healthcare, biomechanics, climate science, and astronomy. Despite existing research addressing irregularities in time series through ordinary differential equations, the challenge of modeling correlations between asynchronous IMTS remains underexplored. To bridge this gap, this study proposes Transformable Patching Graph Neural Networks (t-PatchGNN), which transforms each univariate irregular time series into a series of transformable patches encompassing a varying number of observations with uniform temporal resolution. It seamlessly facilitates local semantics capture and inter-time series correlation modeling while avoiding sequence length explosion in aligned IMTS. Building on the aligned patching outcomes, we then present time-adaptive graph neural networks to model dynamic intertime series correlation based on a series of learned time-varying adaptive graphs. We demonstrate the remarkable superiority of t-PatchGNN on a comprehensive IMTS forecasting benchmark we build, which contains four real-world scientific datasets covering healthcare, biomechanics and climate science, and seventeen competitive baselines adapted from relevant research fields.
|
https://proceedings.mlr.press/v235/zhang24bx.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bx/zhang24bx.pdf
|
https://openreview.net/forum?id=WDgV1BJEW0
|
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
|
https://proceedings.mlr.press/v235/zhang24bx.html
|
Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, Tianlong Chen
|
https://proceedings.mlr.press/v235/zhang24bx.html
|
ICML 2024
|
Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs. A promising solution is to remove non-essential edges to reduce the computational overheads in GNN. Previous literature generally falls into two categories: topology-guided and semantic-guided. The former maintains certain graph topological properties yet often underperforms on GNNs. % due to low integration with neural network training. The latter performs well at lower sparsity on GNNs but faces performance collapse at higher sparsity levels. With this in mind, we propose a new research line and concept termed Graph Sparse Training (GST), which dynamically manipulates sparsity at the data level. Specifically, GST initially constructs a topology & semantic anchor at a low training cost, followed by performing dynamic sparse training to align the sparse graph with the anchor. We introduce the Equilibria Sparsification Principle to guide this process, balancing the preservation of both topological and semantic information. Ultimately, GST produces a sparse graph with maximum topological integrity and no performance degradation. Extensive experiments on 6 datasets and 5 backbones showcase that GST (I) identifies subgraphs at higher graph sparsity levels ($1.67%\sim15.85%$$\uparrow$) than state-of-the-art sparsification methods, (II) preserves more key spectral properties, (III) achieves $1.27-3.42\times$ speedup in GNN inference and (IV) successfully helps graph adversarial defense and graph lottery tickets.
|
https://proceedings.mlr.press/v235/zhang24by.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24by/zhang24by.pdf
|
https://openreview.net/forum?id=W4mLp5KuKl
|
Generalization Analysis for Multi-Label Learning
|
https://proceedings.mlr.press/v235/zhang24by.html
|
Yifan Zhang, Min-Ling Zhang
|
https://proceedings.mlr.press/v235/zhang24by.html
|
ICML 2024
|
Despite great advances in algorithms for multi-label learning, research on the theoretical analysis of generalization is still in the early stage. Some recent theoretical results has investigated the generalization performance of multi-label learning under several evaluation metrics, however, how to reduce the dependency on the number of labels, explicitly introduce label correlations, and quantitatively analyze the impact of various inductive biases in the generalization analysis of multi-label learning is still a crucial and open problem. In an attempt to make up for the gap in the generalization theory of multi-label learning, we develop several novel vector-contraction inequalities, which exploit the Lipschitz continuity of loss functions, and derive generalization bounds with a weaker dependency on the number of labels than the state of the art in the case of decoupling the relationship among different components, which serves as theoretical guarantees for the generalization of multi-label learning. In addition, we derive the generalization bound for Macro-Averaged AUC and analyze its relationship with class-imbalance. The mild bounds without strong assumptions explain the good generalization ability of multi-label learning with first-order label correlations and high-order label correlations induced by norm regularizers.
|
https://proceedings.mlr.press/v235/zhang24bz.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bz/zhang24bz.pdf
|
https://openreview.net/forum?id=CQI3f1U9X1
|
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
|
https://proceedings.mlr.press/v235/zhang24bz.html
|
Yexin Zhang, Chenyi Zhang, Cong Fang, Liwei Wang, Tongyang Li
|
https://proceedings.mlr.press/v235/zhang24bz.html
|
ICML 2024
|
Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let $f_1,\ldots,f_n:\mathbb{R}^d\to\mathbb{R}$ be $\ell$-smooth convex functions and $\psi:\mathbb{R}^d\to\mathbb{R}$ be a $\mu$-strongly convex proximal function. The goal is to find an $\epsilon$-optimal point for $F(\mathbf{x})=\frac{1}{n}\sum_{i=1}^n f_i(\mathbf{x})+\psi(\mathbf{x})$. We give a quantum algorithm with complexity $\tilde{O}\big(n+\sqrt{d}+\sqrt{\ell/\mu}\big(n^{1/3}d^{1/3}+n^{-2/3}d^{5/6}\big)\big)$, improving the classical tight bound $\tilde{\Theta}\big(n+\sqrt{n\ell/\mu}\big)$. We also prove a quantum lower bound $\tilde{\Omega}(n+n^{3/4}(\ell/\mu)^{1/4})$ when $d$ is large enough. Both our quantum upper and lower bounds can extend to the cases where $\psi$ is not necessarily strongly convex, or each $f_i$ is Lipschitz but not necessarily smooth. In addition, when $F$ is nonconvex, our quantum algorithm can find an $\epsilon$-critial point using $\tilde{O}(n+\ell(d^{1/3}n^{1/3}+\sqrt{d})/\epsilon^2)$ queries.
|
https://proceedings.mlr.press/v235/zhang24ca.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ca/zhang24ca.pdf
|
https://openreview.net/forum?id=tQPkzTdaaN
|
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition
|
https://proceedings.mlr.press/v235/zhang24ca.html
|
Ziyang Zhang, Qizhen Zhang, Jakob Nicolaus Foerster
|
https://proceedings.mlr.press/v235/zhang24ca.html
|
ICML 2024
|
Large language models (LLMs) have shown success in many natural language processing tasks. Despite rigorous safety alignment processes, supposedly safety-aligned LLMs like Llama 2 and Claude 2 are still susceptible to jailbreaks, leading to security risks and abuse of the models. One option to mitigate such risks is to augment the LLM with a dedicated "safeguard", which checks the LLM’s inputs or outputs for undesired behaviour. A promising approach is to use the LLM itself as the safeguard. Nonetheless, baseline methods, such as prompting the LLM to self-classify toxic content, demonstrate limited efficacy. We hypothesise that this is due to domain shift: the alignment training imparts a self-censoring behaviour to the model ("Sorry I can’t do that"), while the self-classify approach shifts it to a classification format ("Is this prompt malicious"). In this work, we propose PARDEN, which avoids this domain shift by simply asking the model to repeat its own outputs. PARDEN neither requires finetuning nor white box access to the model. We empirically verify the effectiveness of our method and show that PARDEN significantly outperforms existing jailbreak detection baselines for Llama-2 and Claude-2. We find that PARDEN is particularly powerful in the relevant regime of high True Positive Rate (TPR) and low False Positive Rate (FPR). For instance, for Llama2-7B, at TPR equal to 90%, PARDEN accomplishes a roughly 11x reduction in the FPR from 24.8% to 2.0% on the harmful behaviours dataset. Code and data are available at https://github.com/Ed-Zh/PARDEN.
|
https://proceedings.mlr.press/v235/zhang24cb.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cb/zhang24cb.pdf
|
https://openreview.net/forum?id=q6fXuPLpao
|
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
|
https://proceedings.mlr.press/v235/zhang24cb.html
|
Yu Zhang, Qi Zhang, Zixuan Gong, Yiwei Shi, Yepeng Liu, Duoqian Miao, Yang Liu, Ke Liu, Kun Yi, Wei Fan, Liang Hu, Changwei Wang
|
https://proceedings.mlr.press/v235/zhang24cb.html
|
ICML 2024
|
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms of inefficient data utilization. It relies on a single contrastive supervision for each image-text pair during representation learning, disregarding a substantial amount of valuable information that could offer richer supervision. Additionally, the retention of non-informative tokens leads to increased computational demands and time costs, particularly in CLIP’s ViT image encoder. To address these issues, we propose Multi-Perspective Language-Image Pretraining (MLIP). In MLIP, we leverage the frequency transform’s sensitivity to both high and low-frequency variations, which complements the spatial domain’s sensitivity limited to low-frequency variations only. By incorporating frequency transforms and token-level alignment, we expand CILP’s single supervision into multi-domain and multi-level supervision, enabling a more thorough exploration of informative image features. Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains. This allows us to merge tokens to multi-granularity tokens with a controllable compression rate to accelerate CLIP. Extensive experiments validate the effectiveness of our design.
|
https://proceedings.mlr.press/v235/zhang24cc.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cc/zhang24cc.pdf
|
https://openreview.net/forum?id=QFMcXz6e4Y
|
Lightweight Image Super-Resolution via Flexible Meta Pruning
|
https://proceedings.mlr.press/v235/zhang24cc.html
|
Yulun Zhang, Kai Zhang, Luc Van Gool, Martin Danelljan, Fisher Yu
|
https://proceedings.mlr.press/v235/zhang24cc.html
|
ICML 2024
|
Lightweight image super-resolution (SR) methods have obtained promising results with moderate model complexity. These approaches primarily focus on a lightweight architecture design, but neglect to further reduce network redundancy. While some model compression techniques try to achieve more lightweight SR models with neural architecture search, knowledge distillation, or channel pruning, they typically require considerable extra computational resources or neglect to prune weights. To address these issues, we propose a flexible meta pruning (FMP) for lightweight image SR, where the network channels and weights are pruned simultaneously. Specifically, we control the network sparsity via channel vectors and weight indicators. We feed them into a hypernetwork, whose parameters act as meta-data for the parameters of the SR backbone. Consequently, for each network layer, we conduct structured pruning with channel vectors, which control the output and input channels. Besides, we conduct unstructured pruning with weight indicators to influence the sparsity of kernel weights, resulting in flexible pruning. During pruning, the sparsity of both channel vectors and weight indicators are regularized. We optimize the channel vectors and weight indicators with proximal gradient and SGD. We conduct extensive experiments to investigate critical factors in the flexible channel and weight pruning for image SR, demonstrating the superiority of our FMP when applied to baseline image SR architectures.
|
https://proceedings.mlr.press/v235/zhang24cd.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cd/zhang24cd.pdf
|
https://openreview.net/forum?id=2xbkWiEuR1
|
Offline Training of Language Model Agents with Functions as Learnable Weights
|
https://proceedings.mlr.press/v235/zhang24cd.html
|
Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, Qingyun Wu
|
https://proceedings.mlr.press/v235/zhang24cd.html
|
ICML 2024
|
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a novel paradigm of training LLM agents without modifying the LLM weights, which is particularly useful when the LLMs are difficult or inaccessible for modifications. Inspired by how humans continuously forge tools to adapt to real-world tasks, rather than change our biological structure to fit a static set of tools, we propose to progressively forge agent’s functions to better solve the downstream tasks instead of modifying the LLM weights. By treating the functions as learnable ‘agent parameters’ and leveraging the fundamental idea of model training in artificial intelligence, we develop AgentOptimizer that employs the LLM to update agents’ functions and devise an agent training algorithm with two strategies, roll-back, and early-stop, to streamline the training process. With extensive experiments, we showcase that the agent training paradigm could significantly improve the performance of representative LLM agents in various downstream tasks. We also study the behavior of the agent training regarding aspects like the learning curve and domain transferability.
|
https://proceedings.mlr.press/v235/zhang24ce.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ce/zhang24ce.pdf
|
https://openreview.net/forum?id=0vozy8vstt
|
Efficient Contextual Bandits with Uninformed Feedback Graphs
|
https://proceedings.mlr.press/v235/zhang24ce.html
|
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro
|
https://proceedings.mlr.press/v235/zhang24ce.html
|
ICML 2024
|
Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications. A recent work by [Zhang et al., 2023] studies the contextual version of this problem and proposes an efficient and optimal algorithm via a reduction to online regression. However, their algorithm crucially relies on seeing the feedback graph before making each decision, while in many applications, the feedback graph is uninformed, meaning that it is either only revealed after the learner makes her decision or even never fully revealed at all. This work develops the first contextual algorithms for such uninformed settings, via an efficient reduction to online regression over both the losses and the graphs. Importantly, we show that it is critical to learn the graphs using log loss instead of squared loss to obtain favorable regret guarantees. We also demonstrate the empirical effectiveness of our algorithm on a bidding application using both synthetic and real-world data.
|
https://proceedings.mlr.press/v235/zhang24cf.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cf/zhang24cf.pdf
|
https://openreview.net/forum?id=pktvuR7b5v
|
Efficient Denoising Diffusion via Probabilistic Masking
|
https://proceedings.mlr.press/v235/zhang24cf.html
|
Weizhong Zhang, Zhiwei Zhang, Renjie Pi, Zhongming Jin, Yuan Gao, Jieping Ye, Kani Chen
|
https://proceedings.mlr.press/v235/zhang24cf.html
|
ICML 2024
|
Diffusion models have exhibited remarkable advancements in generating high-quality data. However, a critical drawback is their computationally intensive inference process, which requires a large number of timesteps to generate a single sample. Existing methods address this challenge by decoupling the forward and reverse processes, and they rely on handcrafted rules for sampling acceleration, leading to the risk of discarding important steps. In this paper, we propose an Efficient Denoising Diffusion method via Probabilistic Masking (EDDPM) that can identify and skip the redundant steps during training. To determine whether a timestep should be skipped or not, we employ probabilistic reparameterization to continualize the binary determination mask. The mask distribution parameters are learned jointly with model weights. By incorporating a real-time sparse constraint, our method can effectively identify and eliminate unnecessary steps during the training iterations, thereby improving inference efficiency. Notably, as the model becomes fully trained, the random masks converge to a sparse and deterministic one, retaining only a small number of essential steps. Empirical results demonstrate the superiority of our proposed EDDPM over the state-of-the-art sampling acceleration methods across various domains. EDDPM can generate high-quality samples with only 20% of the steps for time series imputation and achieve 4.89 FID with 5 steps for CIFAR-10. Moreover, when starting from a pretrained model, our method efficiently identifies the most informative timesteps within a single epoch, which demonstrates the potential of EDDPM to be a practical tool to explore large diffusion models with limited resources.
|
https://proceedings.mlr.press/v235/zhang24cg.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24cg/zhang24cg.pdf
|
https://openreview.net/forum?id=gE7qZurGH3
|
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
|
https://proceedings.mlr.press/v235/zhang24cg.html
|
Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You
|
https://proceedings.mlr.press/v235/zhang24cg.html
|
ICML 2024
|
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost for training GNNs. Nevertheless, existing methods often fall short of accurately replicating the original graph for certain datasets, thereby failing to achieve the objective of lossless condensation. To understand this phenomenon, we investigate the potential reasons and reveal that the previous state-of-the-art trajectory matching method provides biased and restricted supervision signals from the original graph when optimizing the condensed one. This significantly limits both the scale and efficacy of the condensed graph. In this paper, we make the first attempt toward lossless graph condensation by bridging the previously neglected supervision signals. Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching. Moreover, we design a loss function to further extract knowledge from the expert trajectories. Theoretical analysis justifies the design of our method and extensive experiments verify its superiority across different datasets. Code is released at https://github.com/NUS-HPC-AI-Lab/GEOM.
|
https://proceedings.mlr.press/v235/zhang24ch.html
|
https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24ch/zhang24ch.pdf
|
https://openreview.net/forum?id=v2o9rRJcEv
|
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
|
https://proceedings.mlr.press/v235/zhang24ch.html
|
Ziyi Zhang, Sen Zhang, Yibing Zhan, Yong Luo, Yonggang Wen, Dacheng Tao
|
https://proceedings.mlr.press/v235/zhang24ch.html
|
ICML 2024
|
Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has emerged as a promising alignment strategy, concerns arise regarding the risk of excessive optimization with learned reward models, which potentially compromises ground-truth performance. In this work, we confront the reward overoptimization problem in diffusion model alignment through the lenses of both inductive and primacy biases. We first identify a mismatch between current methods and the temporal inductive bias inherent in the multi-step denoising process of diffusion models, as a potential source of reward overoptimization. Then, we surprisingly discover that dormant neurons in our critic model act as a regularization against reward overoptimization while active neurons reflect primacy bias. Motivated by these observations, we propose Temporal Diffusion Policy Optimization with critic active neuron Reset (TDPO-R), a policy gradient algorithm that exploits the temporal inductive bias of diffusion models and mitigates the primacy bias stemming from active neurons. Empirical results demonstrate the superior efficacy of our methods in mitigating reward overoptimization. Code is avaliable at https://github.com/ZiyiZhang27/tdpo.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.