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Learning to Perform Physics Experiments via Deep Reinforcement Learning | stat.ML cs.AI cs.CV cs.LG cs.NE physics.soc-ph | When encountering novel objects, humans are able to infer a wide range of
physical properties such as mass, friction and deformability by interacting
with them in a goal driven way. This process of active interaction is in the
same spirit as a scientist performing experiments to discover hidden facts.
Recent advances in artificial intelligence have yielded machines that can
achieve superhuman performance in Go, Atari, natural language processing, and
complex control problems; however, it is not clear that these systems can rival
the scientific intuition of even a young child. In this work we introduce a
basic set of tasks that require agents to estimate properties such as mass and
cohesion of objects in an interactive simulated environment where they can
manipulate the objects and observe the consequences. We found that state of art
deep reinforcement learning methods can learn to perform the experiments
necessary to discover such hidden properties. By systematically manipulating
the problem difficulty and the cost incurred by the agent for performing
experiments, we found that agents learn different strategies that balance the
cost of gathering information against the cost of making mistakes in different
situations.
| Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter
Battaglia, Nando de Freitas | null | 1611.01843 | null | null |
Challenges of Feature Selection for Big Data Analytics | cs.LG | We are surrounded by huge amounts of large-scale high dimensional data. It is
desirable to reduce the dimensionality of data for many learning tasks due to
the curse of dimensionality. Feature selection has shown its effectiveness in
many applications by building simpler and more comprehensive model, improving
learning performance, and preparing clean, understandable data. Recently, some
unique characteristics of big data such as data velocity and data variety
present challenges to the feature selection problem. In this paper, we envision
these challenges of feature selection for big data analytics. In particular, we
first give a brief introduction about feature selection and then detail the
challenges of feature selection for structured, heterogeneous and streaming
data as well as its scalability and stability issues. At last, to facilitate
and promote the feature selection research, we present an open-source feature
selection repository (scikit-feature), which consists of most of current
popular feature selection algorithms.
| Jundong Li, Huan Liu | null | 1611.01875 | null | null |
An Information-Theoretic Framework for Fast and Robust Unsupervised
Learning via Neural Population Infomax | cs.LG cs.AI cs.IT math.IT q-bio.NC stat.ML | A framework is presented for unsupervised learning of representations based
on infomax principle for large-scale neural populations. We use an asymptotic
approximation to the Shannon's mutual information for a large neural population
to demonstrate that a good initial approximation to the global
information-theoretic optimum can be obtained by a hierarchical infomax method.
Starting from the initial solution, an efficient algorithm based on gradient
descent of the final objective function is proposed to learn representations
from the input datasets, and the method works for complete, overcomplete, and
undercomplete bases. As confirmed by numerical experiments, our method is
robust and highly efficient for extracting salient features from input
datasets. Compared with the main existing methods, our algorithm has a distinct
advantage in both the training speed and the robustness of unsupervised
representation learning. Furthermore, the proposed method is easily extended to
the supervised or unsupervised model for training deep structure networks.
| Wentao Huang and Kechen Zhang | null | 1611.01886 | null | null |
Joint Multimodal Learning with Deep Generative Models | stat.ML cs.LG | We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. Recently, some studies handle multiple modalities on deep generative
models, such as variational autoencoders (VAEs). However, these models
typically assume that modalities are forced to have a conditioned relation,
i.e., we can only generate modalities in one direction. To achieve our
objective, we should extract a joint representation that captures high-level
concepts among all modalities and through which we can exchange them
bi-directionally. As described herein, we propose a joint multimodal
variational autoencoder (JMVAE), in which all modalities are independently
conditioned on joint representation. In other words, it models a joint
distribution of modalities. Furthermore, to be able to generate missing
modalities from the remaining modalities properly, we develop an additional
method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's
encoder and prepared networks of respective modalities. Our experiments show
that our proposed method can obtain appropriate joint representation from
multiple modalities and that it can generate and reconstruct them more properly
than conventional VAEs. We further demonstrate that JMVAE can generate multiple
modalities bi-directionally.
| Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo | null | 1611.01891 | null | null |
Decision Tree Classification with Differential Privacy: A Survey | cs.DB cs.LG | Data mining information about people is becoming increasingly important in
the data-driven society of the 21st century. Unfortunately, sometimes there are
real-world considerations that conflict with the goals of data mining;
sometimes the privacy of the people being data mined needs to be considered.
This necessitates that the output of data mining algorithms be modified to
preserve privacy while simultaneously not ruining the predictive power of the
outputted model. Differential privacy is a strong, enforceable definition of
privacy that can be used in data mining algorithms, guaranteeing that nothing
will be learned about the people in the data that could not already be
discovered without their participation. In this survey, we focus on one
particular data mining algorithm -- decision trees -- and how differential
privacy interacts with each of the components that constitute decision tree
algorithms. We analyze both greedy and random decision trees, and the conflicts
that arise when trying to balance privacy requirements with the accuracy of the
model.
| Sam Fletcher, Md Zahidul Islam | null | 1611.01919 | null | null |
Averaged-DQN: Variance Reduction and Stabilization for Deep
Reinforcement Learning | cs.AI cs.LG stat.ML | Instability and variability of Deep Reinforcement Learning (DRL) algorithms
tend to adversely affect their performance. Averaged-DQN is a simple extension
to the DQN algorithm, based on averaging previously learned Q-values estimates,
which leads to a more stable training procedure and improved performance by
reducing approximation error variance in the target values. To understand the
effect of the algorithm, we examine the source of value function estimation
errors and provide an analytical comparison within a simplified model. We
further present experiments on the Arcade Learning Environment benchmark that
demonstrate significantly improved stability and performance due to the
proposed extension.
| Oron Anschel, Nir Baram, Nahum Shimkin | null | 1611.01929 | null | null |
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile
Sensing Data Processing | cs.LG cs.NE cs.NI | Mobile sensing applications usually require time-series inputs from sensors.
Some applications, such as tracking, can use sensed acceleration and rate of
rotation to calculate displacement based on physical system models. Other
applications, such as activity recognition, extract manually designed features
from sensor inputs for classification. Such applications face two challenges.
On one hand, on-device sensor measurements are noisy. For many mobile
applications, it is hard to find a distribution that exactly describes the
noise in practice. Unfortunately, calculating target quantities based on
physical system and noise models is only as accurate as the noise assumptions.
Similarly, in classification applications, although manually designed features
have proven to be effective, it is not always straightforward to find the most
robust features to accommodate diverse sensor noise patterns and user
behaviors. To this end, we propose DeepSense, a deep learning framework that
directly addresses the aforementioned noise and feature customization
challenges in a unified manner. DeepSense integrates convolutional and
recurrent neural networks to exploit local interactions among similar mobile
sensors, merge local interactions of different sensory modalities into global
interactions, and extract temporal relationships to model signal dynamics.
DeepSense thus provides a general signal estimation and classification
framework that accommodates a wide range of applications. We demonstrate the
effectiveness of DeepSense using three representative and challenging tasks:
car tracking with motion sensors, heterogeneous human activity recognition, and
user identification with biometric motion analysis. DeepSense significantly
outperforms the state-of-the-art methods for all three tasks. In addition,
DeepSense is feasible to implement on smartphones due to its moderate energy
consumption and low latency
| Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, Tarek Abdelzaher | null | 1611.01942 | null | null |
Linear Convergence of SVRG in Statistical Estimation | stat.ML cs.LG | SVRG and its variants are among the state of art optimization algorithms for
large scale machine learning problems. It is well known that SVRG converges
linearly when the objective function is strongly convex. However this setup can
be restrictive, and does not include several important formulations such as
Lasso, group Lasso, logistic regression, and some non-convex models including
corrected Lasso and SCAD. In this paper, we prove that, for a class of
statistical M-estimators covering examples mentioned above, SVRG solves the
formulation with {\em a linear convergence rate} without strong convexity or
even convexity. Our analysis makes use of {\em restricted strong convexity},
under which we show that SVRG converges linearly to the fundamental statistical
precision of the model, i.e., the difference between true unknown parameter
$\theta^*$ and the optimal solution $\hat{\theta}$ of the model.
| Chao Qu, Yan Li, Huan Xu | null | 1611.01957 | null | null |
Log-time and Log-space Extreme Classification | cs.LG | We present LTLS, a technique for multiclass and multilabel prediction that
can perform training and inference in logarithmic time and space. LTLS embeds
large classification problems into simple structured prediction problems and
relies on efficient dynamic programming algorithms for inference. We train LTLS
with stochastic gradient descent on a number of multiclass and multilabel
datasets and show that despite its small memory footprint it is often
competitive with existing approaches.
| Kalina Jasinska, Nikos Karampatziakis | null | 1611.01964 | null | null |
Regularizing CNNs with Locally Constrained Decorrelations | cs.LG cs.NE | Regularization is key for deep learning since it allows training more complex
models while keeping lower levels of overfitting. However, the most prevalent
regularizations do not leverage all the capacity of the models since they rely
on reducing the effective number of parameters. Feature decorrelation is an
alternative for using the full capacity of the models but the overfitting
reduction margins are too narrow given the overhead it introduces. In this
paper, we show that regularizing negatively correlated features is an obstacle
for effective decorrelation and present OrthoReg, a novel regularization
technique that locally enforces feature orthogonality. As a result, imposing
locality constraints in feature decorrelation removes interferences between
negatively correlated feature weights, allowing the regularizer to reach higher
decorrelation bounds, and reducing the overfitting more effectively. In
particular, we show that the models regularized with OrthoReg have higher
accuracy bounds even when batch normalization and dropout are present.
Moreover, since our regularization is directly performed on the weights, it is
especially suitable for fully convolutional neural networks, where the weight
space is constant compared to the feature map space. As a result, we are able
to reduce the overfitting of state-of-the-art CNNs on CIFAR-10, CIFAR-100, and
SVHN.
| Pau Rodr\'iguez, Jordi Gonz\`alez, Guillem Cucurull, Josep M. Gonfaus,
Xavier Roca | null | 1611.01967 | null | null |
One Class Splitting Criteria for Random Forests | stat.ML cs.LG | Random Forests (RFs) are strong machine learning tools for classification and
regression. However, they remain supervised algorithms, and no extension of RFs
to the one-class setting has been proposed, except for techniques based on
second-class sampling. This work fills this gap by proposing a natural
methodology to extend standard splitting criteria to the one-class setting,
structurally generalizing RFs to one-class classification. An extensive
benchmark of seven state-of-the-art anomaly detection algorithms is also
presented. This empirically demonstrates the relevance of our approach.
| Nicolas Goix (LTCI), Nicolas Drougard (ISAE), Romain Brault (LTCI),
Ma\"el Chiapino (LTCI) | null | 1611.01971 | null | null |
Fixed-point Factorized Networks | cs.CV cs.LG | In recent years, Deep Neural Networks (DNN) based methods have achieved
remarkable performance in a wide range of tasks and have been among the most
powerful and widely used techniques in computer vision. However, DNN-based
methods are both computational-intensive and resource-consuming, which hinders
the application of these methods on embedded systems like smart phones. To
alleviate this problem, we introduce a novel Fixed-point Factorized Networks
(FFN) for pretrained models to reduce the computational complexity as well as
the storage requirement of networks. The resulting networks have only weights
of -1, 0 and 1, which significantly eliminates the most resource-consuming
multiply-accumulate operations (MACs). Extensive experiments on large-scale
ImageNet classification task show the proposed FFN only requires one-thousandth
of multiply operations with comparable accuracy.
| Peisong Wang and Jian Cheng | null | 1611.01972 | null | null |
Differentiable Functional Program Interpreters | cs.PL cs.LG | Programming by Example (PBE) is the task of inducing computer programs from
input-output examples. It can be seen as a type of machine learning where the
hypothesis space is the set of legal programs in some programming language.
Recent work on differentiable interpreters relaxes the discrete space of
programs into a continuous space so that search over programs can be performed
using gradient-based optimization. While conceptually powerful, so far
differentiable interpreter-based program synthesis has only been capable of
solving very simple problems. In this work, we study modeling choices that
arise when constructing a differentiable programming language and their impact
on the success of synthesis. The main motivation for the modeling choices comes
from functional programming: we study the effect of memory allocation schemes,
immutable data, type systems, and built-in control-flow structures. Empirically
we show that incorporating functional programming ideas into differentiable
programming languages allows us to learn much more complex programs than is
possible with existing differentiable languages.
| John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow | null | 1611.01988 | null | null |
DeepCoder: Learning to Write Programs | cs.LG | We develop a first line of attack for solving programming competition-style
problems from input-output examples using deep learning. The approach is to
train a neural network to predict properties of the program that generated the
outputs from the inputs. We use the neural network's predictions to augment
search techniques from the programming languages community, including
enumerative search and an SMT-based solver. Empirically, we show that our
approach leads to an order of magnitude speedup over the strong non-augmented
baselines and a Recurrent Neural Network approach, and that we are able to
solve problems of difficulty comparable to the simplest problems on programming
competition websites.
| Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin,
Daniel Tarlow | null | 1611.01989 | null | null |
Multi-view Generative Adversarial Networks | cs.LG | Learning over multi-view data is a challenging problem with strong practical
applications. Most related studies focus on the classification point of view
and assume that all the views are available at any time. We consider an
extension of this framework in two directions. First, based on the BiGAN model,
the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from
multi-view inputs. Second, it can deal with missing views and is able to update
its prediction when additional views are provided. We illustrate these
properties on a set of experiments over different datasets.
| Micka\"el Chen and Ludovic Denoyer | 10.1007/978-3-319-71246-8_11 | 1611.02019 | null | null |
Does Distributionally Robust Supervised Learning Give Robust
Classifiers? | stat.ML cs.LG | Distributionally Robust Supervised Learning (DRSL) is necessary for building
reliable machine learning systems. When machine learning is deployed in the
real world, its performance can be significantly degraded because test data may
follow a different distribution from training data. DRSL with f-divergences
explicitly considers the worst-case distribution shift by minimizing the
adversarially reweighted training loss. In this paper, we analyze this DRSL,
focusing on the classification scenario. Since the DRSL is explicitly
formulated for a distribution shift scenario, we naturally expect it to give a
robust classifier that can aggressively handle shifted distributions. However,
surprisingly, we prove that the DRSL just ends up giving a classifier that
exactly fits the given training distribution, which is too pessimistic. This
pessimism comes from two sources: the particular losses used in classification
and the fact that the variety of distributions to which the DRSL tries to be
robust is too wide. Motivated by our analysis, we propose simple DRSL that
overcomes this pessimism and empirically demonstrate its effectiveness.
| Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama | null | 1611.02041 | null | null |
Reinforcement Learning Approach for Parallelization in Filters
Aggregation Based Feature Selection Algorithms | cs.LG cs.AI stat.ML | One of the classical problems in machine learning and data mining is feature
selection. A feature selection algorithm is expected to be quick, and at the
same time it should show high performance. MeLiF algorithm effectively solves
this problem using ensembles of ranking filters. This article describes two
different ways to improve MeLiF algorithm performance with parallelization.
Experiments show that proposed schemes significantly improves algorithm
performance and increase feature selection quality.
| Ivan Smetannikov, Ilya Isaev, Andrey Filchenkov | null | 1611.02047 | null | null |
Reinforcement-based Simultaneous Algorithm and its Hyperparameters
Selection | cs.LG cs.AI stat.ML | Many algorithms for data analysis exist, especially for classification
problems. To solve a data analysis problem, a proper algorithm should be
chosen, and also its hyperparameters should be selected. In this paper, we
present a new method for the simultaneous selection of an algorithm and its
hyperparameters. In order to do so, we reduced this problem to the multi-armed
bandit problem. We consider an algorithm as an arm and algorithm
hyperparameters search during a fixed time as the corresponding arm play. We
also suggest a problem-specific reward function. We performed the experiments
on 10 real datasets and compare the suggested method with the existing one
implemented in Auto-WEKA. The results show that our method is significantly
better in most of the cases and never worse than the Auto-WEKA.
| Valeria Efimova, Andrey Filchenkov, Anatoly Shalyto | null | 1611.02053 | null | null |
Distributed Coordinate Descent for Generalized Linear Models with
Regularization | stat.ML cs.DC cs.LG | Generalized linear model with $L_1$ and $L_2$ regularization is a widely used
technique for solving classification, class probability estimation and
regression problems. With the numbers of both features and examples growing
rapidly in the fields like text mining and clickstream data analysis
parallelization and the use of cluster architectures becomes important. We
present a novel algorithm for fitting regularized generalized linear models in
the distributed environment. The algorithm splits data between nodes by
features, uses coordinate descent on each node and line search to merge results
globally. Convergence proof is provided. A modifications of the algorithm
addresses slow node problem. For an important particular case of logistic
regression we empirically compare our program with several state-of-the art
approaches that rely on different algorithmic and data spitting methods.
Experiments demonstrate that our approach is scalable and superior when
training on large and sparse datasets.
| Ilya Trofimov, Alexander Genkin | null | 1611.02101 | null | null |
Differentiable Programs with Neural Libraries | cs.LG | We develop a framework for combining differentiable programming languages
with neural networks. Using this framework we create end-to-end trainable
systems that learn to write interpretable algorithms with perceptual
components. We explore the benefits of inductive biases for strong
generalization and modularity that come from the program-like structure of our
models. In particular, modularity allows us to learn a library of (neural)
functions which grows and improves as more tasks are solved. Empirically, we
show that this leads to lifelong learning systems that transfer knowledge to
new tasks more effectively than baselines.
| Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow | null | 1611.02109 | null | null |
Neural Networks Designing Neural Networks: Multi-Objective
Hyper-Parameter Optimization | cs.NE cs.LG | Artificial neural networks have gone through a recent rise in popularity,
achieving state-of-the-art results in various fields, including image
classification, speech recognition, and automated control. Both the performance
and computational complexity of such models are heavily dependant on the design
of characteristic hyper-parameters (e.g., number of hidden layers, nodes per
layer, or choice of activation functions), which have traditionally been
optimized manually. With machine learning penetrating low-power mobile and
embedded areas, the need to optimize not only for performance (accuracy), but
also for implementation complexity, becomes paramount. In this work, we present
a multi-objective design space exploration method that reduces the number of
solution networks trained and evaluated through response surface modelling.
Given spaces which can easily exceed 1020 solutions, manually designing a
near-optimal architecture is unlikely as opportunities to reduce network
complexity, while maintaining performance, may be overlooked. This problem is
exacerbated by the fact that hyper-parameters which perform well on specific
datasets may yield sub-par results on others, and must therefore be designed on
a per-application basis. In our work, machine learning is leveraged by training
an artificial neural network to predict the performance of future candidate
networks. The method is evaluated on the MNIST and CIFAR-10 image datasets,
optimizing for both recognition accuracy and computational complexity.
Experimental results demonstrate that the proposed method can closely
approximate the Pareto-optimal front, while only exploring a small fraction of
the design space.
| Sean C. Smithson and Guang Yang and Warren J. Gross and Brett H. Meyer | null | 1611.0212 | null | null |
Unrolled Generative Adversarial Networks | cs.LG stat.ML | We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice, and using the current value of the discriminator, which
is often unstable and leads to poor solutions. We show how this technique
solves the common problem of mode collapse, stabilizes training of GANs with
complex recurrent generators, and increases diversity and coverage of the data
distribution by the generator.
| Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein | null | 1611.02163 | null | null |
Designing Neural Network Architectures using Reinforcement Learning | cs.LG | At present, designing convolutional neural network (CNN) architectures
requires both human expertise and labor. New architectures are handcrafted by
careful experimentation or modified from a handful of existing networks. We
introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to
automatically generate high-performing CNN architectures for a given learning
task. The learning agent is trained to sequentially choose CNN layers using
$Q$-learning with an $\epsilon$-greedy exploration strategy and experience
replay. The agent explores a large but finite space of possible architectures
and iteratively discovers designs with improved performance on the learning
task. On image classification benchmarks, the agent-designed networks
(consisting of only standard convolution, pooling, and fully-connected layers)
beat existing networks designed with the same layer types and are competitive
against the state-of-the-art methods that use more complex layer types. We also
outperform existing meta-modeling approaches for network design on image
classification tasks.
| Bowen Baker, Otkrist Gupta, Nikhil Naik and Ramesh Raskar | null | 1611.02167 | null | null |
Using Social Dynamics to Make Individual Predictions: Variational
Inference with a Stochastic Kinetic Model | stat.ML cs.LG | Social dynamics is concerned primarily with interactions among individuals
and the resulting group behaviors, modeling the temporal evolution of social
systems via the interactions of individuals within these systems. In
particular, the availability of large-scale data from social networks and
sensor networks offers an unprecedented opportunity to predict state-changing
events at the individual level. Examples of such events include disease
transmission, opinion transition in elections, and rumor propagation. Unlike
previous research focusing on the collective effects of social systems, this
study makes efficient inferences at the individual level. In order to cope with
dynamic interactions among a large number of individuals, we introduce the
stochastic kinetic model to capture adaptive transition probabilities and
propose an efficient variational inference algorithm the complexity of which
grows linearly --- rather than exponentially --- with the number of
individuals. To validate this method, we have performed epidemic-dynamics
experiments on wireless sensor network data collected from more than ten
thousand people over three years. The proposed algorithm was used to track
disease transmission and predict the probability of infection for each
individual. Our results demonstrate that this method is more efficient than
sampling while nonetheless achieving high accuracy.
| Zhen Xu, Wen Dong and Sargur Srihari | null | 1611.02181 | null | null |
Trusting SVM for Piecewise Linear CNNs | cs.LG | We present a novel layerwise optimization algorithm for the learning
objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large
class of convolutional neural networks. Specifically, PL-CNNs employ piecewise
linear non-linearities such as the commonly used ReLU and max-pool, and an SVM
classifier as the final layer. The key observation of our approach is that the
problem corresponding to the parameter estimation of a layer can be formulated
as a difference-of-convex (DC) program, which happens to be a latent structured
SVM. We optimize the DC program using the concave-convex procedure, which
requires us to iteratively solve a structured SVM problem. This allows to
design an optimization algorithm with an optimal learning rate that does not
require any tuning. Using the MNIST, CIFAR and ImageNet data sets, we show that
our approach always improves over the state of the art variants of
backpropagation and scales to large data and large network settings.
| Leonard Berrada, Andrew Zisserman, M. Pawan Kumar | null | 1611.02185 | null | null |
CoCoA: A General Framework for Communication-Efficient Distributed
Optimization | cs.LG | The scale of modern datasets necessitates the development of efficient
distributed optimization methods for machine learning. We present a
general-purpose framework for distributed computing environments, CoCoA, that
has an efficient communication scheme and is applicable to a wide variety of
problems in machine learning and signal processing. We extend the framework to
cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net
regularization, and show how earlier work can be derived as a special case. We
provide convergence guarantees for the class of convex regularized loss
minimization objectives, leveraging a novel approach in handling
non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as
we illustrate with an extensive set of experiments on real distributed
datasets.
| Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I.
Jordan, Martin Jaggi | null | 1611.02189 | null | null |
Playing SNES in the Retro Learning Environment | cs.LG cs.AI | Mastering a video game requires skill, tactics and strategy. While these
attributes may be acquired naturally by human players, teaching them to a
computer program is a far more challenging task. In recent years, extensive
research was carried out in the field of reinforcement learning and numerous
algorithms were introduced, aiming to learn how to perform human tasks such as
playing video games. As a result, the Arcade Learning Environment (ALE)
(Bellemare et al., 2013) has become a commonly used benchmark environment
allowing algorithms to train on various Atari 2600 games. In many games the
state-of-the-art algorithms outperform humans. In this paper we introduce a new
learning environment, the Retro Learning Environment --- RLE, that can run
games on the Super Nintendo Entertainment System (SNES), Sega Genesis and
several other gaming consoles. The environment is expandable, allowing for more
video games and consoles to be easily added to the environment, while
maintaining the same interface as ALE. Moreover, RLE is compatible with Python
and Torch. SNES games pose a significant challenge to current algorithms due to
their higher level of complexity and versatility.
| Nadav Bhonker, Shai Rozenberg and Itay Hubara | null | 1611.02205 | null | null |
Minimax-optimal semi-supervised regression on unknown manifolds | stat.ML cs.LG | We consider semi-supervised regression when the predictor variables are drawn
from an unknown manifold. A simple two step approach to this problem is to: (i)
estimate the manifold geodesic distance between any pair of points using both
the labeled and unlabeled instances; and (ii) apply a k nearest neighbor
regressor based on these distance estimates. We prove that given sufficiently
many unlabeled points, this simple method of geodesic kNN regression achieves
the optimal finite-sample minimax bound on the mean squared error, as if the
manifold were known. Furthermore, we show how this approach can be efficiently
implemented, requiring only O(k N log N) operations to estimate the regression
function at all N labeled and unlabeled points. We illustrate this approach on
two datasets with a manifold structure: indoor localization using WiFi
fingerprints and facial pose estimation. In both cases, geodesic kNN is more
accurate and much faster than the popular Laplacian eigenvector regressor.
| Amit Moscovich, Ariel Jaffe, Boaz Nadler | null | 1611.02221 | null | null |
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic | cs.LG | Model-free deep reinforcement learning (RL) methods have been successful in a
wide variety of simulated domains. However, a major obstacle facing deep RL in
the real world is their high sample complexity. Batch policy gradient methods
offer stable learning, but at the cost of high variance, which often requires
large batches. TD-style methods, such as off-policy actor-critic and
Q-learning, are more sample-efficient but biased, and often require costly
hyperparameter sweeps to stabilize. In this work, we aim to develop methods
that combine the stability of policy gradients with the efficiency of
off-policy RL. We present Q-Prop, a policy gradient method that uses a Taylor
expansion of the off-policy critic as a control variate. Q-Prop is both sample
efficient and stable, and effectively combines the benefits of on-policy and
off-policy methods. We analyze the connection between Q-Prop and existing
model-free algorithms, and use control variate theory to derive two variants of
Q-Prop with conservative and aggressive adaptation. We show that conservative
Q-Prop provides substantial gains in sample efficiency over trust region policy
optimization (TRPO) with generalized advantage estimation (GAE), and improves
stability over deep deterministic policy gradient (DDPG), the state-of-the-art
on-policy and off-policy methods, on OpenAI Gym's MuJoCo continuous control
environments.
| Shixiang Gu and Timothy Lillicrap and Zoubin Ghahramani and Richard E.
Turner and Sergey Levine | null | 1611.02247 | null | null |
Hierarchical compositional feature learning | cs.LG cs.AI stat.ML | We introduce the hierarchical compositional network (HCN), a directed
generative model able to discover and disentangle, without supervision, the
building blocks of a set of binary images. The building blocks are binary
features defined hierarchically as a composition of some of the features in the
layer immediately below, arranged in a particular manner. At a high level, HCN
is similar to a sigmoid belief network with pooling. Inference and learning in
HCN are very challenging and existing variational approximations do not work
satisfactorily. A main contribution of this work is to show that both can be
addressed using max-product message passing (MPMP) with a particular schedule
(no EM required). Also, using MPMP as an inference engine for HCN makes new
tasks simple: adding supervision information, classifying images, or performing
inpainting all correspond to clamping some variables of the model to their
known values and running MPMP on the rest. When used for classification, fast
inference with HCN has exactly the same functional form as a convolutional
neural network (CNN) with linear activations and binary weights. However, HCN's
features are qualitatively very different.
| Miguel L\'azaro-Gredilla, Yi Liu, D. Scott Phoenix, Dileep George | null | 1611.02252 | null | null |
Learning Time Series Detection Models from Temporally Imprecise Labels | stat.ML cs.LG | In this paper, we consider a new low-quality label learning problem: learning
time series detection models from temporally imprecise labels. In this problem,
the data consist of a set of input time series, and supervision is provided by
a sequence of noisy time stamps corresponding to the occurrence of positive
class events. Such temporally imprecise labels commonly occur in areas like
mobile health research where human annotators are tasked with labeling the
occurrence of very short duration events. We propose a general learning
framework for this problem that can accommodate different base classifiers and
noise models. We present results on real mobile health data showing that the
proposed framework significantly outperforms a number of alternatives including
assuming that the label time stamps are noise-free, transforming the problem
into the multiple instance learning framework, and learning on labels that were
manually re-aligned.
| Roy J. Adams, Benjamin M. Marlin | null | 1611.02258 | null | null |
Memory-augmented Attention Modelling for Videos | cs.CV cs.LG cs.NE | We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already looked at and described. This enables not only more
effective local attention, but tractable consideration of the video sequence
while generating each word. Evaluation on the challenging and popular MSVD and
Charades datasets demonstrates that the proposed architecture outperforms
previous video description approaches without requiring external temporal video
features.
| Rasool Fakoor, Abdel-rahman Mohamed, Margaret Mitchell, Sing Bing
Kang, Pushmeet Kohli | null | 1611.02261 | null | null |
Gaussian Attention Model and Its Application to Knowledge Base Embedding
and Question Answering | stat.ML cs.AI cs.CL cs.LG | We propose the Gaussian attention model for content-based neural memory
access. With the proposed attention model, a neural network has the additional
degree of freedom to control the focus of its attention from a laser sharp
attention to a broad attention. It is applicable whenever we can assume that
the distance in the latent space reflects some notion of semantics. We use the
proposed attention model as a scoring function for the embedding of a knowledge
base into a continuous vector space and then train a model that performs
question answering about the entities in the knowledge base. The proposed
attention model can handle both the propagation of uncertainty when following a
series of relations and also the conjunction of conditions in a natural way. On
a dataset of soccer players who participated in the FIFA World Cup 2014, we
demonstrate that our model can handle both path queries and conjunctive queries
well.
| Liwen Zhang and John Winn and Ryota Tomioka | null | 1611.02266 | null | null |
Optimal Binary Autoencoding with Pairwise Correlations | cs.LG cs.AI stat.ML | We formulate learning of a binary autoencoder as a biconvex optimization
problem which learns from the pairwise correlations between encoded and decoded
bits. Among all possible algorithms that use this information, ours finds the
autoencoder that reconstructs its inputs with worst-case optimal loss. The
optimal decoder is a single layer of artificial neurons, emerging entirely from
the minimax loss minimization, and with weights learned by convex optimization.
All this is reflected in competitive experimental results, demonstrating that
binary autoencoding can be done efficiently by conveying information in
pairwise correlations in an optimal fashion.
| Akshay Balsubramani | null | 1611.02268 | null | null |
Learning Influence Functions from Incomplete Observations | cs.SI cs.LG stat.ML | We study the problem of learning influence functions under incomplete
observations of node activations. Incomplete observations are a major concern
as most (online and real-world) social networks are not fully observable. We
establish both proper and improper PAC learnability of influence functions
under randomly missing observations. Proper PAC learnability under the
Discrete-Time Linear Threshold (DLT) and Discrete-Time Independent Cascade
(DIC) models is established by reducing incomplete observations to complete
observations in a modified graph. Our improper PAC learnability result applies
for the DLT and DIC models as well as the Continuous-Time Independent Cascade
(CIC) model. It is based on a parametrization in terms of reachability
features, and also gives rise to an efficient and practical heuristic.
Experiments on synthetic and real-world datasets demonstrate the ability of our
method to compensate even for a fairly large fraction of missing observations.
| Xinran He, Ke Xu, David Kempe and Yan Liu | null | 1611.02305 | null | null |
Learning from Untrusted Data | cs.LG cs.AI cs.CC cs.CR math.ST stat.TH | The vast majority of theoretical results in machine learning and statistics
assume that the available training data is a reasonably reliable reflection of
the phenomena to be learned or estimated. Similarly, the majority of machine
learning and statistical techniques used in practice are brittle to the
presence of large amounts of biased or malicious data. In this work we consider
two frameworks in which to study estimation, learning, and optimization in the
presence of significant fractions of arbitrary data.
The first framework, list-decodable learning, asks whether it is possible to
return a list of answers, with the guarantee that at least one of them is
accurate. For example, given a dataset of $n$ points for which an unknown
subset of $\alpha n$ points are drawn from a distribution of interest, and no
assumptions are made about the remaining $(1-\alpha)n$ points, is it possible
to return a list of $\operatorname{poly}(1/\alpha)$ answers, one of which is
correct? The second framework, which we term the semi-verified learning model,
considers the extent to which a small dataset of trusted data (drawn from the
distribution in question) can be leveraged to enable the accurate extraction of
information from a much larger but untrusted dataset (of which only an
$\alpha$-fraction is drawn from the distribution).
We show strong positive results in both settings, and provide an algorithm
for robust learning in a very general stochastic optimization setting. This
general result has immediate implications for robust estimation in a number of
settings, including for robustly estimating the mean of distributions with
bounded second moments, robustly learning mixtures of such distributions, and
robustly finding planted partitions in random graphs in which significant
portions of the graph have been perturbed by an adversary.
| Moses Charikar and Jacob Steinhardt and Gregory Valiant | null | 1611.02315 | null | null |
Adversarial Ladder Networks | cs.NE cs.LG stat.ML | The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializing the weights of the network and after these weights are
refined with the use of supervised data. On the other hand adversarial noise
has improved the results of clas- sical supervised learning. Recently, a new
neural network topology called Ladder Network, where the key idea is based in
some properties of hierar- chichal latent variable models, has been proposed as
a technique to train a neural network using supervised and unsupervised data at
the same time with what is called semi-supervised learning. This technique has
reached state of the art classification. In this work we add adversarial noise
to the ladder network and get state of the art classification, with several
important conclusions on how adversarial noise can help in addition with new
possible lines of investi- gation. We also propose an alternative to add
adversarial noise to unsu- pervised data.
| Juan Maro\~nas Molano, Alberto Albiol Colomer, Roberto Paredes
Palacios | null | 1611.0232 | null | null |
Neural Taylor Approximations: Convergence and Exploration in Rectifier
Networks | cs.LG cs.NE stat.ML | Modern convolutional networks, incorporating rectifiers and max-pooling, are
neither smooth nor convex; standard guarantees therefore do not apply.
Nevertheless, methods from convex optimization such as gradient descent and
Adam are widely used as building blocks for deep learning algorithms. This
paper provides the first convergence guarantee applicable to modern convnets,
which furthermore matches a lower bound for convex nonsmooth functions. The key
technical tool is the neural Taylor approximation -- a straightforward
application of Taylor expansions to neural networks -- and the associated
Taylor loss. Experiments on a range of optimizers, layers, and tasks provide
evidence that the analysis accurately captures the dynamics of neural
optimization. The second half of the paper applies the Taylor approximation to
isolate the main difficulty in training rectifier nets -- that gradients are
shattered -- and investigates the hypothesis that, by exploring the space of
activation configurations more thoroughly, adaptive optimizers such as RMSProp
and Adam are able to converge to better solutions.
| David Balduzzi, Brian McWilliams, Tony Butler-Yeoman | null | 1611.02345 | null | null |
NonSTOP: A NonSTationary Online Prediction Method for Time Series | stat.ML cs.LG | We present online prediction methods for time series that let us explicitly
handle nonstationary artifacts (e.g. trend and seasonality) present in most
real time series. Specifically, we show that applying appropriate
transformations to such time series before prediction can lead to improved
theoretical and empirical prediction performance. Moreover, since these
transformations are usually unknown, we employ the learning with experts
setting to develop a fully online method (NonSTOP-NonSTationary Online
Prediction) for predicting nonstationary time series. This framework allows for
seasonality and/or other trends in univariate time series and cointegration in
multivariate time series. Our algorithms and regret analysis subsume recent
related work while significantly expanding the applicability of such methods.
For all the methods, we provide sub-linear regret bounds using relaxed
assumptions. The theoretical guarantees do not fully capture the benefits of
the transformations, thus we provide a data-dependent analysis of the
follow-the-leader algorithm that provides insight into the success of using
such transformations. We support all of our results with experiments on
simulated and real data.
| Christopher Xie, Avleen Bijral, Juan Lavista Ferres | null | 1611.02365 | null | null |
Divide and Conquer Networks | cs.LG stat.ML | We consider the learning of algorithmic tasks by mere observation of
input-output pairs. Rather than studying this as a black-box discrete
regression problem with no assumption whatsoever on the input-output mapping,
we concentrate on tasks that are amenable to the principle of divide and
conquer, and study what are its implications in terms of learning. This
principle creates a powerful inductive bias that we leverage with neural
architectures that are defined recursively and dynamically, by learning two
scale-invariant atomic operations: how to split a given input into smaller
sets, and how to merge two partially solved tasks into a larger partial
solution. Our model can be trained in weakly supervised environments, namely by
just observing input-output pairs, and in even weaker environments, using a
non-differentiable reward signal. Moreover, thanks to the dynamic aspect of our
architecture, we can incorporate the computational complexity as a
regularization term that can be optimized by backpropagation. We demonstrate
the flexibility and efficiency of the Divide-and-Conquer Network on several
combinatorial and geometric tasks: convex hull, clustering, knapsack and
euclidean TSP. Thanks to the dynamic programming nature of our model, we show
significant improvements in terms of generalization error and computational
complexity.
| Alex Nowak-Vila, David Folqu\'e and Joan Bruna | null | 1611.02401 | null | null |
An Efficient Approach to Boosting Performance of Deep Spiking Network
Training | cs.LG cs.NE | Nowadays deep learning is dominating the field of machine learning with
state-of-the-art performance in various application areas. Recently, spiking
neural networks (SNNs) have been attracting a great deal of attention, notably
owning to their power efficiency, which can potentially allow us to implement a
low-power deep learning engine suitable for real-time/mobile applications.
However, implementing SNN-based deep learning remains challenging, especially
gradient-based training of SNNs by error backpropagation. We cannot simply
propagate errors through SNNs in conventional way because of the property of
SNNs that process discrete data in the form of a series. Consequently, most of
the previous studies employ a workaround technique, which first trains a
conventional weighted-sum deep neural network and then maps the learning
weights to the SNN under training, instead of training SNN parameters directly.
In order to eliminate this workaround, recently proposed is a new class of SNN
named deep spiking networks (DSNs), which can be trained directly (without a
mapping from conventional deep networks) by error backpropagation with
stochastic gradient descent. In this paper, we show that the initialization of
the membrane potential on the backward path is an important step in DSN
training, through diverse experiments performed under various conditions.
Furthermore, we propose a simple and efficient method that can improve DSN
training by controlling the initial membrane potential on the backward path. In
our experiments, adopting the proposed approach allowed us to boost the
performance of DSN training in terms of converging time and accuracy.
| Seongsik Park, Sang-gil Lee, Hyunha Nam, Sungroh Yoon | null | 1611.02416 | null | null |
Domain Adaptation with L2 constraints for classifying images from
different endoscope systems | cs.CV cs.LG | This paper proposes a method for domain adaptation that extends the maximum
margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2
distance constraints between samples of different domains; thus, our method is
denoted as MMDTL2. Motivated by the differences between the images taken by
narrow band imaging (NBI) endoscopic devices, we utilize different NBI devices
as different domains and estimate the transformations between samples of
different domains, i.e., image samples taken by different NBI endoscope
systems. We first formulate the problem in the primal form, and then derive the
dual form with much lesser computational costs as compared to the naive
approach. From our experimental results using NBI image datasets from two
different NBI endoscopic devices, we find that MMDTL2 is better than MMDT and
also support vector machines without adaptation, especially when NBI image
features are high-dimensional and the per-class training samples are greater
than 20.
| Toru Tamaki, Shoji Sonoyama, Takio Kurita, Tsubasa Hirakawa, Bisser
Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno,
Shinji Tanaka, Kazuaki Chayama | null | 1611.02443 | null | null |
Cognitive Discriminative Mappings for Rapid Learning | cs.AI cs.LG cs.NE | Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input from sensory memory by leveraging the information retrieved from
long-term memory. We present a simple and intuitive technique called cognitive
discriminative mappings (CDM) to explore the cognitive problem. First, CDM
separates and clusters the data instances retrieved from long-term memory into
distinct classes with a discrimination method in working memory when a sensory
input triggers the algorithm. CDM then maps each sensory data instance to be as
close as possible to the median point of the data group with the same class.
The experimental results demonstrate that the CDM approach is effective for
learning the discriminative features of supervised classifications with few
training sensory input instances.
| Wen-Chieh Fang and Yi-ting Chiang | null | 1611.02512 | null | null |
PixelSNE: Visualizing Fast with Just Enough Precision via Pixel-Aligned
Stochastic Neighbor Embedding | cs.LG | Embedding and visualizing large-scale high-dimensional data in a
two-dimensional space is an important problem since such visualization can
reveal deep insights out of complex data. Most of the existing embedding
approaches, however, run on an excessively high precision, ignoring the fact
that at the end, embedding outputs are converted into coarse-grained discrete
pixel coordinates in a screen space. Motivated by such an observation and
directly considering pixel coordinates in an embedding optimization process, we
accelerate Barnes-Hut tree-based t-distributed stochastic neighbor embedding
(BH-SNE), known as a state-of-the-art 2D embedding method, and propose a novel
method called PixelSNE, a highly-efficient, screen resolution-driven 2D
embedding method with a linear computational complexity in terms of the number
of data items. Our experimental results show the significantly fast running
time of PixelSNE by a large margin against BH-SNE, while maintaining the
minimal degradation in the embedding quality. Finally, the source code of our
method is publicly available at https://github.com/awesome-davian/PixelSNE
| Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park,
Jaegul Choo | null | 1611.02568 | null | null |
Gradients of Counterfactuals | cs.LG cs.CV | Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenomena is indeed widespread, across many inputs.
We propose to examine interior gradients, which are gradients of
counterfactual inputs constructed by scaling down the original input. We apply
our method to the GoogleNet architecture for object recognition in images, as
well as a ligand-based virtual screening network with categorical features and
an LSTM based language model for the Penn Treebank dataset. We visualize how
interior gradients better capture feature importance. Furthermore, interior
gradients are applicable to a wide variety of deep networks, and have the
attribution property that the feature importance scores sum to the the
prediction score.
Best of all, interior gradients can be computed just as easily as gradients.
In contrast, previous methods are complex to implement, which hinders practical
adoption.
| Mukund Sundararajan, Ankur Taly, Qiqi Yan | null | 1611.02639 | null | null |
Deep Unsupervised Clustering with Gaussian Mixture Variational
Autoencoders | cs.LG cs.NE stat.ML | We study a variant of the variational autoencoder model (VAE) with a Gaussian
mixture as a prior distribution, with the goal of performing unsupervised
clustering through deep generative models. We observe that the known problem of
over-regularisation that has been shown to arise in regular VAEs also manifests
itself in our model and leads to cluster degeneracy. We show that a heuristic
called minimum information constraint that has been shown to mitigate this
effect in VAEs can also be applied to improve unsupervised clustering
performance with our model. Furthermore we analyse the effect of this heuristic
and provide an intuition of the various processes with the help of
visualizations. Finally, we demonstrate the performance of our model on
synthetic data, MNIST and SVHN, showing that the obtained clusters are
distinct, interpretable and result in achieving competitive performance on
unsupervised clustering to the state-of-the-art results.
| Nat Dilokthanakul, Pedro A.M. Mediano, Marta Garnelo, Matthew C.H.
Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan | null | 1611.02648 | null | null |
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks | cs.CL cs.AI cs.LG | Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address this problem. Our model strongly outperforms prior methods in the order
discrimination task and a novel task of ordering abstracts from scientific
articles. Furthermore, our work shows that useful text representations can be
obtained by learning to order sentences. Visualizing the learned sentence
representations shows that the model captures high-level logical structure in
paragraphs. Our representations perform comparably to state-of-the-art
pre-training methods on sentence similarity and paraphrase detection tasks.
| Lajanugen Logeswaran, Honglak Lee, Dragomir Radev | null | 1611.02654 | null | null |
Unsupervised Pretraining for Sequence to Sequence Learning | cs.CL cs.LG cs.NE | This work presents a general unsupervised learning method to improve the
accuracy of sequence to sequence (seq2seq) models. In our method, the weights
of the encoder and decoder of a seq2seq model are initialized with the
pretrained weights of two language models and then fine-tuned with labeled
data. We apply this method to challenging benchmarks in machine translation and
abstractive summarization and find that it significantly improves the
subsequent supervised models. Our main result is that pretraining improves the
generalization of seq2seq models. We achieve state-of-the art results on the
WMT English$\rightarrow$German task, surpassing a range of methods using both
phrase-based machine translation and neural machine translation. Our method
achieves a significant improvement of 1.3 BLEU from the previous best models on
both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human
evaluations on abstractive summarization and find that our method outperforms a
purely supervised learning baseline in a statistically significant manner.
| Prajit Ramachandran, Peter J. Liu, Quoc V. Le | null | 1611.02683 | null | null |
Variational Lossy Autoencoder | cs.LG stat.ML | Representation learning seeks to expose certain aspects of observed data in a
learned representation that's amenable to downstream tasks like classification.
For instance, a good representation for 2D images might be one that describes
only global structure and discards information about detailed texture. In this
paper, we present a simple but principled method to learn such global
representations by combining Variational Autoencoder (VAE) with neural
autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE
model allows us to have control over what the global latent code can learn and
, by designing the architecture accordingly, we can force the global latent
code to discard irrelevant information such as texture in 2D images, and hence
the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging
autoregressive models as both prior distribution $p(z)$ and decoding
distribution $p(x|z)$, we can greatly improve generative modeling performance
of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and
Caltech-101 Silhouettes density estimation tasks.
| Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla
Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel | null | 1611.02731 | null | null |
Recursive Regression with Neural Networks: Approximating the HJI PDE
Solution | cs.LG math.DS | The majority of methods used to compute approximations to the
Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the
discretization of the state space to perform dynamic programming updates. This
type of approach is known to suffer from the curse of dimensionality due to the
exponential growth in grid points with the state dimension. In this work we
present an approximate dynamic programming algorithm that computes an
approximation of the solution of the HJI PDE by alternating between solving a
regression problem and solving a minimax problem using a feedforward neural
network as the function approximator. We find that this method requires less
memory to run and to store the approximation than traditional gridding methods,
and we test it on a few systems of two, three and six dimensions.
| Vicen\c{c} Rubies-Royo, Claire Tomlin | null | 1611.02739 | null | null |
Recursive Decomposition for Nonconvex Optimization | cs.AI cs.LG stat.ML | Continuous optimization is an important problem in many areas of AI,
including vision, robotics, probabilistic inference, and machine learning.
Unfortunately, most real-world optimization problems are nonconvex, causing
standard convex techniques to find only local optima, even with extensions like
random restarts and simulated annealing. We observe that, in many cases, the
local modes of the objective function have combinatorial structure, and thus
ideas from combinatorial optimization can be brought to bear. Based on this, we
propose a problem-decomposition approach to nonconvex optimization. Similarly
to DPLL-style SAT solvers and recursive conditioning in probabilistic
inference, our algorithm, RDIS, recursively sets variables so as to simplify
and decompose the objective function into approximately independent
sub-functions, until the remaining functions are simple enough to be optimized
by standard techniques like gradient descent. The variables to set are chosen
by graph partitioning, ensuring decomposition whenever possible. We show
analytically that RDIS can solve a broad class of nonconvex optimization
problems exponentially faster than gradient descent with random restarts.
Experimentally, RDIS outperforms standard techniques on problems like structure
from motion and protein folding.
| Abram L. Friesen and Pedro Domingos | null | 1611.02755 | null | null |
Delving into Transferable Adversarial Examples and Black-box Attacks | cs.LG | An intriguing property of deep neural networks is the existence of
adversarial examples, which can transfer among different architectures. These
transferable adversarial examples may severely hinder deep neural network-based
applications. Previous works mostly study the transferability using small scale
datasets. In this work, we are the first to conduct an extensive study of the
transferability over large models and a large scale dataset, and we are also
the first to study the transferability of targeted adversarial examples with
their target labels. We study both non-targeted and targeted adversarial
examples, and show that while transferable non-targeted adversarial examples
are easy to find, targeted adversarial examples generated using existing
approaches almost never transfer with their target labels. Therefore, we
propose novel ensemble-based approaches to generating transferable adversarial
examples. Using such approaches, we observe a large proportion of targeted
adversarial examples that are able to transfer with their target labels for the
first time. We also present some geometric studies to help understanding the
transferable adversarial examples. Finally, we show that the adversarial
examples generated using ensemble-based approaches can successfully attack
Clarifai.com, which is a black-box image classification system.
| Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song | null | 1611.0277 | null | null |
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning | cs.AI cs.LG cs.NE stat.ML | Deep reinforcement learning (deep RL) has been successful in learning
sophisticated behaviors automatically; however, the learning process requires a
huge number of trials. In contrast, animals can learn new tasks in just a few
trials, benefiting from their prior knowledge about the world. This paper seeks
to bridge this gap. Rather than designing a "fast" reinforcement learning
algorithm, we propose to represent it as a recurrent neural network (RNN) and
learn it from data. In our proposed method, RL$^2$, the algorithm is encoded in
the weights of the RNN, which are learned slowly through a general-purpose
("slow") RL algorithm. The RNN receives all information a typical RL algorithm
would receive, including observations, actions, rewards, and termination flags;
and it retains its state across episodes in a given Markov Decision Process
(MDP). The activations of the RNN store the state of the "fast" RL algorithm on
the current (previously unseen) MDP. We evaluate RL$^2$ experimentally on both
small-scale and large-scale problems. On the small-scale side, we train it to
solve randomly generated multi-arm bandit problems and finite MDPs. After
RL$^2$ is trained, its performance on new MDPs is close to human-designed
algorithms with optimality guarantees. On the large-scale side, we test RL$^2$
on a vision-based navigation task and show that it scales up to
high-dimensional problems.
| Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever,
Pieter Abbeel | null | 1611.02779 | null | null |
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models
with KL-control | cs.LG cs.AI | This paper proposes a general method for improving the structure and quality
of sequences generated by a recurrent neural network (RNN), while maintaining
information originally learned from data, as well as sample diversity. An RNN
is first pre-trained on data using maximum likelihood estimation (MLE), and the
probability distribution over the next token in the sequence learned by this
model is treated as a prior policy. Another RNN is then trained using
reinforcement learning (RL) to generate higher-quality outputs that account for
domain-specific incentives while retaining proximity to the prior policy of the
MLE RNN. To formalize this objective, we derive novel off-policy RL methods for
RNNs from KL-control. The effectiveness of the approach is demonstrated on two
applications; 1) generating novel musical melodies, and 2) computational
molecular generation. For both problems, we show that the proposed method
improves the desired properties and structure of the generated sequences, while
maintaining information learned from data.
| Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, Jos\'e Miguel
Hern\'andez-Lobato, Richard E. Turner, Douglas Eck | null | 1611.02796 | null | null |
Online Learning for Wireless Distributed Computing | cs.LG | There has been a growing interest for Wireless Distributed Computing (WDC),
which leverages collaborative computing over multiple wireless devices. WDC
enables complex applications that a single device cannot support individually.
However, the problem of assigning tasks over multiple devices becomes
challenging in the dynamic environments encountered in real-world settings,
considering that the resource availability and channel conditions change over
time in unpredictable ways due to mobility and other factors. In this paper, we
formulate a task assignment problem as an online learning problem using an
adversarial multi-armed bandit framework. We propose MABSTA, a novel online
learning algorithm that learns the performance of unknown devices and channel
qualities continually through exploratory probing and makes task assignment
decisions by exploiting the gained knowledge. For maximal adaptability, MABSTA
is designed to make no stochastic assumption about the environment. We analyze
it mathematically and provide a worst-case performance guarantee for any
dynamic environment. We also compare it with the optimal offline policy as well
as other baselines via emulations on trace-data obtained from a wireless IoT
testbed, and show that it offers competitive and robust performance in all
cases. To the best of our knowledge, MABSTA is the first online algorithm in
this domain of task assignment problems and provides provable performance
guarantee.
| Yi-Hsuan Kao, Kwame Wright, Bhaskar Krishnamachari, Fan Bai | null | 1611.0283 | null | null |
Lie-Access Neural Turing Machines | cs.NE cs.LG | External neural memory structures have recently become a popular tool for
algorithmic deep learning (Graves et al. 2014, Weston et al. 2014). These
models generally utilize differentiable versions of traditional discrete
memory-access structures (random access, stacks, tapes) to provide the storage
necessary for computational tasks. In this work, we argue that these neural
memory systems lack specific structure important for relative indexing, and
propose an alternative model, Lie-access memory, that is explicitly designed
for the neural setting. In this paradigm, memory is accessed using a continuous
head in a key-space manifold. The head is moved via Lie group actions, such as
shifts or rotations, generated by a controller, and memory access is performed
by linear smoothing in key space. We argue that Lie groups provide a natural
generalization of discrete memory structures, such as Turing machines, as they
provide inverse and identity operators while maintaining differentiability. To
experiment with this approach, we implement a simplified Lie-access neural
Turing machine (LANTM) with different Lie groups. We find that this approach is
able to perform well on a range of algorithmic tasks.
| Greg Yang, Alexander M. Rush | null | 1611.02854 | null | null |
Audio Visual Speech Recognition using Deep Recurrent Neural Networks | cs.CV cs.CL cs.LG | In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are then used to perform a non-linear dimensionality reduction
of the visual features using a deep bottleneck network. Audio and visual
features are fused and used to train a fusion RNN. The use of bottleneck
features for visual modality helps the model to converge properly during
training. Our system is evaluated on GRID corpus. Our results show that
presence of visual modality gives significant improvement in character error
rate (CER) at various levels of noise even when the model is trained without
noisy data. We also provide a comparison of two fusion methods: feature fusion
and decision fusion.
| Abhinav Thanda, Shankar M Venkatesan | null | 1611.02879 | null | null |
Heter-LP: A heterogeneous label propagation algorithm and its
application in drug repositioning | q-bio.QM cs.LG | Drug repositioning offers an effective solution to drug discovery, saving
both time and resources by finding new indications for existing drugs.
Typically, a drug takes effect via its protein targets in the cell. As a
result, it is necessary for drug development studies to conduct an
investigation into the interrelationships of drugs, protein targets, and
diseases. Although previous studies have made a strong case for the
effectiveness of integrative network-based methods for predicting these
interrelationships, little progress has been achieved in this regard within
drug repositioning research. Moreover, the interactions of new drugs and
targets (lacking any known targets and drugs, respectively) cannot be
accurately predicted by most established methods. In this paper, we propose a
novel semi-supervised heterogeneous label propagation algorithm named Heter-LP,
which applies both local as well as global network features for data
integration. To predict drug-target, disease-target, and drug-disease
associations, we use information about drugs, diseases, and targets as
collected from multiple sources at different levels. Our algorithm integrates
these various types of data into a heterogeneous network and implements a label
propagation algorithm to find new interactions. Statistical analyses of 10-fold
cross-validation results and experimental analysis support the effectiveness of
the proposed algorithm.
| Maryam Lotfi Shahreza, Nasser Ghadiri, Seyed Rasul Mossavi, Jaleh
Varshosaz, James Green | 10.1016/j.jbi.2017.03.006 | 1611.02945 | null | null |
A Unified Maximum Likelihood Approach for Optimal Distribution Property
Estimation | cs.IT cs.DS cs.LG math.IT | The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.
| Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh | null | 1611.0296 | null | null |
Attributing Hacks | cs.LG cs.CR stat.AP | In this paper we describe an algorithm for estimating the provenance of hacks
on websites. That is, given properties of sites and the temporal occurrence of
attacks, we are able to attribute individual attacks to joint causes and
vulnerabilities, as well as estimating the evolution of these vulnerabilities
over time. Specifically, we use hazard regression with a time-varying additive
hazard function parameterized in a generalized linear form. The activation
coefficients on each feature are continuous-time functions over time. We
formulate the problem of learning these functions as a constrained variational
maximum likelihood estimation problem with total variation penalty and show
that the optimal solution is a 0th order spline (a piecewise constant function)
with a finite number of known knots. This allows the inference problem to be
solved efficiently and at scale by solving a finite dimensional optimization
problem. Extensive experiments on real data sets show that our method
significantly outperforms Cox's proportional hazard model. We also conduct a
case study and verify that the fitted functions are indeed recovering
vulnerable features and real-life events such as the release of code to exploit
these features in hacker blogs.
| Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua
Zheng, Jun Zhou | null | 1611.03021 | null | null |
Incremental Sequence Learning | cs.LG cs.NE | Deep learning research over the past years has shown that by increasing the
scope or difficulty of the learning problem over time, increasingly complex
learning problems can be addressed. We study incremental learning in the
context of sequence learning, using generative RNNs in the form of multi-layer
recurrent Mixture Density Networks. While the potential of incremental or
curriculum learning to enhance learning is known, indiscriminate application of
the principle does not necessarily lead to improvement, and it is essential
therefore to know which forms of incremental or curriculum learning have a
positive effect. This research contributes to that aim by comparing three
instantiations of incremental or curriculum learning.
We introduce Incremental Sequence Learning, a simple incremental approach to
sequence learning. Incremental Sequence Learning starts out by using only the
first few steps of each sequence as training data. Each time a performance
criterion has been reached, the length of the parts of the sequences used for
training is increased.
We introduce and make available a novel sequence learning task and data set:
predicting and classifying MNIST pen stroke sequences. We find that Incremental
Sequence Learning greatly speeds up sequence learning and reaches the best test
performance level of regular sequence learning 20 times faster, reduces the
test error by 74%, and in general performs more robustly; it displays lower
variance and achieves sustained progress after all three comparison methods
have stopped improving. The other instantiations of curriculum learning do not
result in any noticeable improvement. A trained sequence prediction model is
also used in transfer learning to the task of sequence classification, where it
is found that transfer learning realizes improved classification performance
compared to methods that learn to classify from scratch.
| Edwin D. de Jong | null | 1611.03068 | null | null |
Fairness in Reinforcement Learning | cs.LG | We initiate the study of fairness in reinforcement learning, where the
actions of a learning algorithm may affect its environment and future rewards.
Our fairness constraint requires that an algorithm never prefers one action
over another if the long-term (discounted) reward of choosing the latter action
is higher. Our first result is negative: despite the fact that fairness is
consistent with the optimal policy, any learning algorithm satisfying fairness
must take time exponential in the number of states to achieve non-trivial
approximation to the optimal policy. We then provide a provably fair polynomial
time algorithm under an approximate notion of fairness, thus establishing an
exponential gap between exact and approximate fairness
| Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern,
Aaron Roth | null | 1611.03071 | null | null |
Energy-efficient Machine Learning in Silicon: A Communications-inspired
Approach | cs.LG cs.AR | This position paper advocates a communications-inspired approach to the
design of machine learning systems on energy-constrained embedded `always-on'
platforms. The communications-inspired approach has two versions - 1) a
deterministic version where existing low-power communication IC design methods
are repurposed, and 2) a stochastic version referred to as Shannon-inspired
statistical information processing employing information-based metrics,
statistical error compensation (SEC), and retraining-based methods to implement
ML systems on stochastic circuit/device fabrics operating at the limits of
energy-efficiency. The communications-inspired approach has the potential to
fully leverage the opportunities afforded by ML algorithms and applications in
order to address the challenges inherent in their deployment on
energy-constrained platforms.
| Naresh R. Shanbhag | null | 1611.03109 | null | null |
A Modular Theory of Feature Learning | cs.LG | Learning representations of data, and in particular learning features for a
subsequent prediction task, has been a fruitful area of research delivering
impressive empirical results in recent years. However, relatively little is
understood about what makes a representation `good'. We propose the idea of a
risk gap induced by representation learning for a given prediction context,
which measures the difference in the risk of some learner using the learned
features as compared to the original inputs. We describe a set of sufficient
conditions for unsupervised representation learning to provide a benefit, as
measured by this risk gap. These conditions decompose the problem of when
representation learning works into its constituent parts, which can be
separately evaluated using an unlabeled sample, suitable domain-specific
assumptions about the joint distribution, and analysis of the feature learner
and subsequent supervised learner. We provide two examples of such conditions
in the context of specific properties of the unlabeled distribution, namely
when the data lies close to a low-dimensional manifold and when it forms
clusters. We compare our approach to a recently proposed analysis of
semi-supervised learning.
| Daniel McNamara, Cheng Soon Ong, Robert C. Williamson | null | 1611.03125 | null | null |
Diverse Neural Network Learns True Target Functions | cs.LG stat.ML | Neural networks are a powerful class of functions that can be trained with
simple gradient descent to achieve state-of-the-art performance on a variety of
applications. Despite their practical success, there is a paucity of results
that provide theoretical guarantees on why they are so effective. Lying in the
center of the problem is the difficulty of analyzing the non-convex loss
function with potentially numerous local minima and saddle points. Can neural
networks corresponding to the stationary points of the loss function learn the
true target function? If yes, what are the key factors contributing to such
nice optimization properties?
In this paper, we answer these questions by analyzing one-hidden-layer neural
networks with ReLU activation, and show that despite the non-convexity, neural
networks with diverse units have no spurious local minima. We bypass the
non-convexity issue by directly analyzing the first order optimality condition,
and show that the loss can be made arbitrarily small if the minimum singular
value of the "extended feature matrix" is large enough. We make novel use of
techniques from kernel methods and geometric discrepancy, and identify a new
relation linking the smallest singular value to the spectrum of a kernel
function associated with the activation function and to the diversity of the
units. Our results also suggest a novel regularization function to promote unit
diversity for potentially better generalization.
| Bo Xie, Yingyu Liang, Le Song | null | 1611.03131 | null | null |
Using Neural Networks to Compute Approximate and Guaranteed Feasible
Hamilton-Jacobi-Bellman PDE Solutions | cs.LG | To sidestep the curse of dimensionality when computing solutions to
Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an
algorithm that leverages a neural network to approximate the value function. We
show that our final approximation of the value function generates near optimal
controls which are guaranteed to successfully drive the system to a target
state. Our framework is not dependent on state space discretization, leading to
a significant reduction in computation time and space complexity in comparison
with dynamic programming-based approaches. Using this grid-free approach also
enables us to plan over longer time horizons with relatively little additional
computation overhead. Unlike many previous neural network HJB PDE approximating
formulations, our approximation is strictly conservative and hence any
trajectories we generate will be strictly feasible. For demonstration, we
specialize our new general framework to the Dubins car model and discuss how
the framework can be applied to other models with higher-dimensional state
spaces.
| Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin | null | 1611.03158 | null | null |
SoK: Applying Machine Learning in Security - A Survey | cs.CR cs.LG | The idea of applying machine learning(ML) to solve problems in security
domains is almost 3 decades old. As information and communications grow more
ubiquitous and more data become available, many security risks arise as well as
appetite to manage and mitigate such risks. Consequently, research on applying
and designing ML algorithms and systems for security has grown fast, ranging
from intrusion detection systems(IDS) and malware classification to security
policy management(SPM) and information leak checking. In this paper, we
systematically study the methods, algorithms, and system designs in academic
publications from 2008-2015 that applied ML in security domains. 98 percent of
the surveyed papers appeared in the 6 highest-ranked academic security
conferences and 1 conference known for pioneering ML applications in security.
We examine the generalized system designs, underlying assumptions,
measurements, and use cases in active research. Our examinations lead to 1) a
taxonomy on ML paradigms and security domains for future exploration and
exploitation, and 2) an agenda detailing open and upcoming challenges. Based on
our survey, we also suggest a point of view that treats security as a game
theory problem instead of a batch-trained ML problem.
| Heju Jiang, Jasvir Nagra, Parvez Ahammad | null | 1611.03186 | null | null |
Low Data Drug Discovery with One-shot Learning | cs.LG stat.ML | Recent advances in machine learning have made significant contributions to
drug discovery. Deep neural networks in particular have been demonstrated to
provide significant boosts in predictive power when inferring the properties
and activities of small-molecule compounds. However, the applicability of these
techniques has been limited by the requirement for large amounts of training
data. In this work, we demonstrate how one-shot learning can be used to
significantly lower the amounts of data required to make meaningful predictions
in drug discovery applications. We introduce a new architecture, the residual
LSTM embedding, that, when combined with graph convolutional neural networks,
significantly improves the ability to learn meaningful distance metrics over
small-molecules. We open source all models introduced in this work as part of
DeepChem, an open-source framework for deep-learning in drug discovery.
| Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, and Vijay Pande | null | 1611.03199 | null | null |
Ultimate tensorization: compressing convolutional and FC layers alike | cs.LG | Convolutional neural networks excel in image recognition tasks, but this
comes at the cost of high computational and memory complexity. To tackle this
problem, [1] developed a tensor factorization framework to compress
fully-connected layers. In this paper, we focus on compressing convolutional
layers. We show that while the direct application of the tensor framework [1]
to the 4-dimensional kernel of convolution does compress the layer, we can do
better. We reshape the convolutional kernel into a tensor of higher order and
factorize it. We combine the proposed approach with the previous work to
compress both convolutional and fully-connected layers of a network and achieve
80x network compression rate with 1.1% accuracy drop on the CIFAR-10 dataset.
| Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov | null | 1611.03214 | null | null |
Learning to Play Guess Who? and Inventing a Grounded Language as a
Consequence | cs.AI cs.CL cs.LG cs.MA | Acquiring your first language is an incredible feat and not easily
duplicated. Learning to communicate using nothing but a few pictureless books,
a corpus, would likely be impossible even for humans. Nevertheless, this is the
dominating approach in most natural language processing today. As an
alternative, we propose the use of situated interactions between agents as a
driving force for communication, and the framework of Deep Recurrent Q-Networks
for evolving a shared language grounded in the provided environment. We task
the agents with interactive image search in the form of the game Guess Who?.
The images from the game provide a non trivial environment for the agents to
discuss and a natural grounding for the concepts they decide to encode in their
communication. Our experiments show that the agents learn not only to encode
physical concepts in their words, i.e. grounding, but also that the agents
learn to hold a multi-step dialogue remembering the state of the dialogue from
step to step.
| Emilio Jorge, Mikael K{\aa}geb\"ack, Fredrik D. Johansson, Emil
Gustavsson | null | 1611.03218 | null | null |
Faster Kernel Ridge Regression Using Sketching and Preconditioning | cs.NA cs.DS cs.LG math.NA | Kernel Ridge Regression (KRR) is a simple yet powerful technique for
non-parametric regression whose computation amounts to solving a linear system.
This system is usually dense and highly ill-conditioned. In addition, the
dimensions of the matrix are the same as the number of data points, so direct
methods are unrealistic for large-scale datasets. In this paper, we propose a
preconditioning technique for accelerating the solution of the aforementioned
linear system. The preconditioner is based on random feature maps, such as
random Fourier features, which have recently emerged as a powerful technique
for speeding up and scaling the training of kernel-based methods, such as
kernel ridge regression, by resorting to approximations. However, random
feature maps only provide crude approximations to the kernel function, so
delivering state-of-the-art results by directly solving the approximated system
requires the number of random features to be very large. We show that random
feature maps can be much more effective in forming preconditioners, since under
certain conditions a not-too-large number of random features is sufficient to
yield an effective preconditioner. We empirically evaluate our method and show
it is highly effective for datasets of up to one million training examples.
| Haim Avron and Kenneth L. Clarkson and David P. Woodruff | null | 1611.0322 | null | null |
Sharper Bounds for Regularized Data Fitting | cs.DS cs.LG cs.NA math.NA | We study matrix sketching methods for regularized variants of linear
regression, low rank approximation, and canonical correlation analysis. Our
main focus is on sketching techniques which preserve the objective function
value for regularized problems, which is an area that has remained largely
unexplored. We study regularization both in a fairly broad setting, and in the
specific context of the popular and widely used technique of ridge
regularization; for the latter, as applied to each of these problems, we show
algorithmic resource bounds in which the {\em statistical dimension} appears in
places where in previous bounds the rank would appear. The statistical
dimension is always smaller than the rank, and decreases as the amount of
regularization increases. In particular, for the ridge low-rank approximation
problem $\min_{Y,X} \lVert YX - A \rVert_F^2 + \lambda \lVert Y\rVert_F^2 +
\lambda\lVert X \rVert_F^2$, where $Y\in\mathbb{R}^{n\times k}$ and
$X\in\mathbb{R}^{k\times d}$, we give an approximation algorithm needing \[
O(\mathtt{nnz}(A)) + \tilde{O}((n+d)\varepsilon^{-1}k \min\{k,
\varepsilon^{-1}\mathtt{sd}_\lambda(Y^*)\})+
\mathtt{poly}(\mathtt{sd}_\lambda(Y^*) \varepsilon^{-1}) \] time, where
$s_{\lambda}(Y^*)\le k$ is the statistical dimension of $Y^*$, $Y^*$ is an
optimal $Y$, $\varepsilon$ is an error parameter, and $\mathtt{nnz}(A)$ is the
number of nonzero entries of $A$.This is faster than prior work, even when
$\lambda=0$.
We also study regularization in a much more general setting. For example, we
obtain sketching-based algorithms for the low-rank approximation problem
$\min_{X,Y} \lVert YX - A \rVert_F^2 + f(Y,X)$ where $f(\cdot,\cdot)$ is a
regularizing function satisfying some very general conditions (chiefly,
invariance under orthogonal transformations).
| Haim Avron and Kenneth L. Clarkson and David P. Woodruff | null | 1611.03225 | null | null |
Policy Search with High-Dimensional Context Variables | stat.ML cs.LG | Direct contextual policy search methods learn to improve policy parameters
and simultaneously generalize these parameters to different context or task
variables. However, learning from high-dimensional context variables, such as
camera images, is still a prominent problem in many real-world tasks. A naive
application of unsupervised dimensionality reduction methods to the context
variables, such as principal component analysis, is insufficient as
task-relevant input may be ignored. In this paper, we propose a contextual
policy search method in the model-based relative entropy stochastic search
framework with integrated dimensionality reduction. We learn a model of the
reward that is locally quadratic in both the policy parameters and the context
variables. Furthermore, we perform supervised linear dimensionality reduction
on the context variables by nuclear norm regularization. The experimental
results show that the proposed method outperforms naive dimensionality
reduction via principal component analysis and a state-of-the-art contextual
policy search method.
| Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan
Peters, Masashi Sugiyama | null | 1611.03231 | null | null |
Disentangling factors of variation in deep representations using
adversarial training | cs.LG stat.ML | We introduce a conditional generative model for learning to disentangle the
hidden factors of variation within a set of labeled observations, and separate
them into complementary codes. One code summarizes the specified factors of
variation associated with the labels. The other summarizes the remaining
unspecified variability. During training, the only available source of
supervision comes from our ability to distinguish among different observations
belonging to the same class. Examples of such observations include images of a
set of labeled objects captured at different viewpoints, or recordings of set
of speakers dictating multiple phrases. In both instances, the intra-class
diversity is the source of the unspecified factors of variation: each object is
observed at multiple viewpoints, and each speaker dictates multiple phrases.
Learning to disentangle the specified factors from the unspecified ones becomes
easier when strong supervision is possible. Suppose that during training, we
have access to pairs of images, where each pair shows two different objects
captured from the same viewpoint. This source of alignment allows us to solve
our task using existing methods. However, labels for the unspecified factors
are usually unavailable in realistic scenarios where data acquisition is not
strictly controlled. We address the problem of disentanglement in this more
general setting by combining deep convolutional autoencoders with a form of
adversarial training. Both factors of variation are implicitly captured in the
organization of the learned embedding space, and can be used for solving
single-image analogies. Experimental results on synthetic and real datasets
show that the proposed method is capable of generalizing to unseen classes and
intra-class variabilities.
| Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann
LeCun | null | 1611.03383 | null | null |
Learning an Astronomical Catalog of the Visible Universe through
Scalable Bayesian Inference | cs.DC astro-ph.IM cs.LG stat.AP stat.ML | Celeste is a procedure for inferring astronomical catalogs that attains
state-of-the-art scientific results. To date, Celeste has been scaled to at
most hundreds of megabytes of astronomical images: Bayesian posterior inference
is notoriously demanding computationally. In this paper, we report on a
scalable, parallel version of Celeste, suitable for learning catalogs from
modern large-scale astronomical datasets. Our algorithmic innovations include a
fast numerical optimization routine for Bayesian posterior inference and a
statistically efficient scheme for decomposing astronomical optimization
problems into subproblems.
Our scalable implementation is written entirely in Julia, a new high-level
dynamic programming language designed for scientific and numerical computing.
We use Julia's high-level constructs for shared and distributed memory
parallelism, and demonstrate effective load balancing and efficient scaling on
up to 8192 Xeon cores on the NERSC Cori supercomputer.
| Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David
Schlegel, Jon McAuliffe and Prabhat | null | 1611.03404 | null | null |
Binomial Checkpointing for Arbitrary Programs with No User Annotation | cs.PL cs.LG cs.MS | Heretofore, automatic checkpointing at procedure-call boundaries, to reduce
the space complexity of reverse mode, has been provided by systems like
Tapenade. However, binomial checkpointing, or treeverse, has only been provided
in Automatic Differentiation (AD) systems in special cases, e.g., through
user-provided pragmas on DO loops in Tapenade, or as the nested taping
mechanism in adol-c for time integration processes, which requires that user
code be refactored. We present a framework for applying binomial checkpointing
to arbitrary code with no special annotation or refactoring required. This is
accomplished by applying binomial checkpointing directly to a program trace.
This trace is produced by a general-purpose checkpointing mechanism that is
orthogonal to AD.
| Jeffrey Mark Siskind and Barak A. Pearlmutter | null | 1611.0341 | null | null |
DiffSharp: An AD Library for .NET Languages | cs.MS cs.LG | DiffSharp is an algorithmic differentiation or automatic differentiation (AD)
library for the .NET ecosystem, which is targeted by the C# and F# languages,
among others. The library has been designed with machine learning applications
in mind, allowing very succinct implementations of models and optimization
routines. DiffSharp is implemented in F# and exposes forward and reverse AD
operators as general nestable higher-order functions, usable by any .NET
language. It provides high-performance linear algebra primitives---scalars,
vectors, and matrices, with a generalization to tensors underway---that are
fully supported by all the AD operators, and which use a BLAS/LAPACK backend
via the highly optimized OpenBLAS library. DiffSharp currently uses operator
overloading, but we are developing a transformation-based version of the
library using F#'s "code quotation" metaprogramming facility. Work on a
CUDA-based GPU backend is also underway.
| At{\i}l{\i}m G\"une\c{s} Baydin and Barak A. Pearlmutter and Jeffrey
Mark Siskind | null | 1611.03423 | null | null |
Multi-Task Multiple Kernel Relationship Learning | stat.ML cs.LG | This paper presents a novel multitask multiple kernel learning framework that
efficiently learns the kernel weights leveraging the relationship across
multiple tasks. The idea is to automatically infer this task relationship in
the \textit{RKHS} space corresponding to the given base kernels. The problem is
formulated as a regularization-based approach called \textit{Multi-Task
Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the
task relationship matrix from the weights learned from latent feature spaces of
task-specific base kernels. Unlike in previous work, the proposed formulation
allows one to incorporate prior knowledge for simultaneously learning several
related tasks. We propose an alternating minimization algorithm to learn the
model parameters, kernel weights and task relationship matrix. In order to
tackle large-scale problems, we further propose a two-stage \textit{MK-MTRL}
online learning algorithm and show that it significantly reduces the
computational time, and also achieves performance comparable to that of the
joint learning framework. Experimental results on benchmark datasets show that
the proposed formulations outperform several state-of-the-art multitask
learning methods.
| Keerthiram Murugesan, Jaime Carbonell | null | 1611.03427 | null | null |
Importance Sampling with Unequal Support | cs.LG cs.AI stat.ML | Importance sampling is often used in machine learning when training and
testing data come from different distributions. In this paper we propose a new
variant of importance sampling that can reduce the variance of importance
sampling-based estimates by orders of magnitude when the supports of the
training and testing distributions differ. After motivating and presenting our
new importance sampling estimator, we provide a detailed theoretical analysis
that characterizes both its bias and variance relative to the ordinary
importance sampling estimator (in various settings, which include cases where
ordinary importance sampling is biased, while our new estimator is not, and
vice versa). We conclude with an example of how our new importance sampling
estimator can be used to improve estimates of how well a new treatment policy
for diabetes will work for an individual, using only data from when the
individual used a previous treatment policy.
| Philip S. Thomas and Emma Brunskill | null | 1611.03451 | null | null |
Statistical Query Lower Bounds for Robust Estimation of High-dimensional
Gaussians and Gaussian Mixtures | cs.LG cs.CC cs.DS cs.IT math.IT math.ST stat.TH | We describe a general technique that yields the first {\em Statistical Query
lower bounds} for a range of fundamental high-dimensional learning problems
involving Gaussian distributions. Our main results are for the problems of (1)
learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of
a single unknown Gaussian distribution. For each of these problems, we show a
{\em super-polynomial gap} between the (information-theoretic) sample
complexity and the computational complexity of {\em any} Statistical Query
algorithm for the problem. Our SQ lower bound for Problem (1) is qualitatively
matched by known learning algorithms for GMMs. Our lower bound for Problem (2)
implies that the accuracy of the robust learning algorithm
in~\cite{DiakonikolasKKLMS16} is essentially best possible among all
polynomial-time SQ algorithms.
Our SQ lower bounds are attained via a unified moment-matching technique that
is useful in other contexts and may be of broader interest. Our technique
yields nearly-tight lower bounds for a number of related unsupervised
estimation problems. Specifically, for the problems of (3) robust covariance
estimation in spectral norm, and (4) robust sparse mean estimation, we
establish a quadratic {\em statistical--computational tradeoff} for SQ
algorithms, matching known upper bounds. Finally, our technique can be used to
obtain tight sample complexity lower bounds for high-dimensional {\em testing}
problems. Specifically, for the classical problem of robustly {\em testing} an
unknown mean (known covariance) Gaussian, our technique implies an
information-theoretic sample lower bound that scales {\em linearly} in the
dimension. Our sample lower bound matches the sample complexity of the
corresponding robust {\em learning} problem and separates the sample complexity
of robust testing from standard (non-robust) testing.
| Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart | null | 1611.03473 | null | null |
Understanding deep learning requires rethinking generalization | cs.LG | Despite their massive size, successful deep artificial neural networks can
exhibit a remarkably small difference between training and test performance.
Conventional wisdom attributes small generalization error either to properties
of the model family, or to the regularization techniques used during training.
Through extensive systematic experiments, we show how these traditional
approaches fail to explain why large neural networks generalize well in
practice. Specifically, our experiments establish that state-of-the-art
convolutional networks for image classification trained with stochastic
gradient methods easily fit a random labeling of the training data. This
phenomenon is qualitatively unaffected by explicit regularization, and occurs
even if we replace the true images by completely unstructured random noise. We
corroborate these experimental findings with a theoretical construction showing
that simple depth two neural networks already have perfect finite sample
expressivity as soon as the number of parameters exceeds the number of data
points as it usually does in practice.
We interpret our experimental findings by comparison with traditional models.
| Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol
Vinyals | null | 1611.0353 | null | null |
The Sum-Product Theorem: A Foundation for Learning Tractable Models | cs.LG cs.AI | Inference in expressive probabilistic models is generally intractable, which
makes them difficult to learn and limits their applicability. Sum-product
networks are a class of deep models where, surprisingly, inference remains
tractable even when an arbitrary number of hidden layers are present. In this
paper, we generalize this result to a much broader set of learning problems:
all those where inference consists of summing a function over a semiring. This
includes satisfiability, constraint satisfaction, optimization, integration,
and others. In any semiring, for summation to be tractable it suffices that the
factors of every product have disjoint scopes. This unifies and extends many
previous results in the literature. Enforcing this condition at learning time
thus ensures that the learned models are tractable. We illustrate the power and
generality of this approach by applying it to a new type of structured
prediction problem: learning a nonconvex function that can be globally
optimized in polynomial time. We show empirically that this greatly outperforms
the standard approach of learning without regard to the cost of optimization.
| Abram L. Friesen and Pedro Domingos | null | 1611.03553 | null | null |
Simple and Efficient Parallelization for Probabilistic Temporal Tensor
Factorization | stat.ML cs.LG | Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm
to model the temporal tensor data. It leverages a time constraint to capture
the evolving properties of tensor data. Nowadays the exploding dataset demands
a large scale PTTF analysis, and a parallel solution is critical to accommodate
the trend. Whereas, the parallelization of PTTF still remains unexplored. In
this paper, we propose a simple yet efficient Parallel Probabilistic Temporal
Tensor Factorization, referred to as P$^2$T$^2$F, to provide a scalable PTTF
solution. P$^2$T$^2$F is fundamentally disparate from existing parallel tensor
factorizations by considering the probabilistic decomposition and the temporal
effects of tensor data. It adopts a new tensor data split strategy to subdivide
a large tensor into independent sub-tensors, the computation of which is
inherently parallel. We train P$^2$T$^2$F with an efficient algorithm of
stochastic Alternating Direction Method of Multipliers, and show that the
convergence is guaranteed. Experiments on several real-word tensor datasets
demonstrate that P$^2$T$^2$F is a highly effective and efficiently scalable
algorithm dedicated for large scale probabilistic temporal tensor analysis.
| Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael
Lyu | null | 1611.03578 | null | null |
Collision-based Testers are Optimal for Uniformity and Closeness | cs.DS cs.IT cs.LG math.IT math.ST stat.TH | We study the fundamental problems of (i) uniformity testing of a discrete
distribution, and (ii) closeness testing between two discrete distributions
with bounded $\ell_2$-norm. These problems have been extensively studied in
distribution testing and sample-optimal estimators are known for
them~\cite{Paninski:08, CDVV14, VV14, DKN:15}.
In this work, we show that the original collision-based testers proposed for
these problems ~\cite{GRdist:00, BFR+:00} are sample-optimal, up to constant
factors. Previous analyses showed sample complexity upper bounds for these
testers that are optimal as a function of the domain size $n$, but suboptimal
by polynomial factors in the error parameter $\epsilon$. Our main contribution
is a new tight analysis establishing that these collision-based testers are
information-theoretically optimal, up to constant factors, both in the
dependence on $n$ and in the dependence on $\epsilon$.
| Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price | null | 1611.03579 | null | null |
UTCNN: a Deep Learning Model of Stance Classificationon on Social Media
Text | cs.CL cs.AI cs.LG | Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
comments on posts. UTCNN not only works on social media texts, but also
analyzes texts in forums and message boards. Experiments performed on Chinese
Facebook data and English online debate forum data show that UTCNN achieves a
0.755 macro-average f-score for supportive, neutral, and unsupportive stance
classes on Facebook data, which is significantly better than models in which
either user, topic, or comment information is withheld. This model design
greatly mitigates the lack of data for the minor class without the use of
oversampling. In addition, UTCNN yields a 0.842 accuracy on English online
debate forum data, which also significantly outperforms results from previous
work as well as other deep learning models, showing that UTCNN performs well
regardless of language or platform.
| Wei-Fan Chen and Lun-Wei Ku | null | 1611.03599 | null | null |
Greedy Step Averaging: A parameter-free stochastic optimization method | cs.LG | In this paper we present the greedy step averaging(GSA) method, a
parameter-free stochastic optimization algorithm for a variety of machine
learning problems. As a gradient-based optimization method, GSA makes use of
the information from the minimizer of a single sample's loss function, and
takes average strategy to calculate reasonable learning rate sequence. While
most existing gradient-based algorithms introduce an increasing number of hyper
parameters or try to make a trade-off between computational cost and
convergence rate, GSA avoids the manual tuning of learning rate and brings in
no more hyper parameters or extra cost. We perform exhaustive numerical
experiments for logistic and softmax regression to compare our method with the
other state of the art ones on 16 datasets. Results show that GSA is robust on
various scenarios.
| Xiatian Zhang, Fan Yao, Yongjun Tian | null | 1611.03608 | null | null |
Learning to Navigate in Complex Environments | cs.AI cs.CV cs.LG cs.RO | Learning to navigate in complex environments with dynamic elements is an
important milestone in developing AI agents. In this work we formulate the
navigation question as a reinforcement learning problem and show that data
efficiency and task performance can be dramatically improved by relying on
additional auxiliary tasks leveraging multimodal sensory inputs. In particular
we consider jointly learning the goal-driven reinforcement learning problem
with auxiliary depth prediction and loop closure classification tasks. This
approach can learn to navigate from raw sensory input in complicated 3D mazes,
approaching human-level performance even under conditions where the goal
location changes frequently. We provide detailed analysis of the agent
behaviour, its ability to localise, and its network activity dynamics, showing
that the agent implicitly learns key navigation abilities.
| Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J.
Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray
Kavukcuoglu, Dharshan Kumaran and Raia Hadsell | null | 1611.03673 | null | null |
Hierarchical Object Detection with Deep Reinforcement Learning | cs.CV cs.LG | We present a method for performing hierarchical object detection in images
guided by a deep reinforcement learning agent. The key idea is to focus on
those parts of the image that contain richer information and zoom on them. We
train an intelligent agent that, given an image window, is capable of deciding
where to focus the attention among five different predefined region candidates
(smaller windows). This procedure is iterated providing a hierarchical image
analysis.We compare two different candidate proposal strategies to guide the
object search: with and without overlap. Moreover, our work compares two
different strategies to extract features from a convolutional neural network
for each region proposal: a first one that computes new feature maps for each
region proposal, and a second one that computes the feature maps for the whole
image to later generate crops for each region proposal. Experiments indicate
better results for the overlapping candidate proposal strategy and a loss of
performance for the cropped image features due to the loss of spatial
resolution. We argue that, while this loss seems unavoidable when working with
large amounts of object candidates, the much more reduced amount of region
proposals generated by our reinforcement learning agent allows considering to
extract features for each location without sharing convolutional computation
among regions.
| Miriam Bellver, Xavier Giro-i-Nieto, Ferran Marques and Jordi Torres | null | 1611.03718 | null | null |
Tricks from Deep Learning | cs.LG stat.ML | The deep learning community has devised a diverse set of methods to make
gradient optimization, using large datasets, of large and highly complex models
with deeply cascaded nonlinearities, practical. Taken as a whole, these methods
constitute a breakthrough, allowing computational structures which are quite
wide, very deep, and with an enormous number and variety of free parameters to
be effectively optimized. The result now dominates much of practical machine
learning, with applications in machine translation, computer vision, and speech
recognition. Many of these methods, viewed through the lens of algorithmic
differentiation (AD), can be seen as either addressing issues with the gradient
itself, or finding ways of achieving increased efficiency using tricks that are
AD-related, but not provided by current AD systems.
The goal of this paper is to explain not just those methods of most relevance
to AD, but also the technical constraints and mindset which led to their
discovery. After explaining this context, we present a "laundry list" of
methods developed by the deep learning community. Two of these are discussed in
further mathematical detail: a way to dramatically reduce the size of the tape
when performing reverse-mode AD on a (theoretically) time-reversible process
like an ODE integrator; and a new mathematical insight that allows for the
implementation of a stochastic Newton's method.
| At{\i}l{\i}m G\"une\c{s} Baydin and Barak A. Pearlmutter and Jeffrey
Mark Siskind | null | 1611.03777 | null | null |
Towards the Science of Security and Privacy in Machine Learning | cs.CR cs.LG | Advances in machine learning (ML) in recent years have enabled a dizzying
array of applications such as data analytics, autonomous systems, and security
diagnostics. ML is now pervasive---new systems and models are being deployed in
every domain imaginable, leading to rapid and widespread deployment of software
based inference and decision making. There is growing recognition that ML
exposes new vulnerabilities in software systems, yet the technical community's
understanding of the nature and extent of these vulnerabilities remains
limited. We systematize recent findings on ML security and privacy, focusing on
attacks identified on these systems and defenses crafted to date. We articulate
a comprehensive threat model for ML, and categorize attacks and defenses within
an adversarial framework. Key insights resulting from works both in the ML and
security communities are identified and the effectiveness of approaches are
related to structural elements of ML algorithms and the data used to train
them. We conclude by formally exploring the opposing relationship between model
accuracy and resilience to adversarial manipulation. Through these
explorations, we show that there are (possibly unavoidable) tensions between
model complexity, accuracy, and resilience that must be calibrated for the
environments in which they will be used.
| Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, Michael Wellman | null | 1611.03814 | null | null |
Recovery Guarantee of Non-negative Matrix Factorization via Alternating
Updates | cs.LG cs.DS stat.ML | Non-negative matrix factorization is a popular tool for decomposing data into
feature and weight matrices under non-negativity constraints. It enjoys
practical success but is poorly understood theoretically. This paper proposes
an algorithm that alternates between decoding the weights and updating the
features, and shows that assuming a generative model of the data, it provably
recovers the ground-truth under fairly mild conditions. In particular, its only
essential requirement on features is linear independence. Furthermore, the
algorithm uses ReLU to exploit the non-negativity for decoding the weights, and
thus can tolerate adversarial noise that can potentially be as large as the
signal, and can tolerate unbiased noise much larger than the signal. The
analysis relies on a carefully designed coupling between two potential
functions, which we believe is of independent interest.
| Yuanzhi Li, Yingyu Liang, Andrej Risteski | null | 1611.03819 | null | null |
Learning to Learn without Gradient Descent by Gradient Descent | stat.ML cs.LG | We learn recurrent neural network optimizers trained on simple synthetic
functions by gradient descent. We show that these learned optimizers exhibit a
remarkable degree of transfer in that they can be used to efficiently optimize
a broad range of derivative-free black-box functions, including Gaussian
process bandits, simple control objectives, global optimization benchmarks and
hyper-parameter tuning tasks. Up to the training horizon, the learned
optimizers learn to trade-off exploration and exploitation, and compare
favourably with heavily engineered Bayesian optimization packages for
hyper-parameter tuning.
| Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha
Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas | null | 1611.03824 | null | null |
A Connection between Generative Adversarial Networks, Inverse
Reinforcement Learning, and Energy-Based Models | cs.LG cs.AI | Generative adversarial networks (GANs) are a recently proposed class of
generative models in which a generator is trained to optimize a cost function
that is being simultaneously learned by a discriminator. While the idea of
learning cost functions is relatively new to the field of generative modeling,
learning costs has long been studied in control and reinforcement learning (RL)
domains, typically for imitation learning from demonstrations. In these fields,
learning cost function underlying observed behavior is known as inverse
reinforcement learning (IRL) or inverse optimal control. While at first the
connection between cost learning in RL and cost learning in generative modeling
may appear to be a superficial one, we show in this paper that certain IRL
methods are in fact mathematically equivalent to GANs. In particular, we
demonstrate an equivalence between a sample-based algorithm for maximum entropy
IRL and a GAN in which the generator's density can be evaluated and is provided
as an additional input to the discriminator. Interestingly, maximum entropy IRL
is a special case of an energy-based model. We discuss the interpretation of
GANs as an algorithm for training energy-based models, and relate this
interpretation to other recent work that seeks to connect GANs and EBMs. By
formally highlighting the connection between GANs, IRL, and EBMs, we hope that
researchers in all three communities can better identify and apply transferable
ideas from one domain to another, particularly for developing more stable and
scalable algorithms: a major challenge in all three domains.
| Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine | null | 1611.03852 | null | null |
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM | stat.ML cs.LG | Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is
used as the building block in energy-based deep generative models. Due to
numerical stability and quantifiability of the likelihood, RBM is commonly used
with Bernoulli units. Here, we consider an alternative member of exponential
family RBM with leaky rectified linear units -- called leaky RBM. We first
study the joint and marginal distributions of leaky RBM under different
leakiness, which provides us important insights by connecting the leaky RBM
model and truncated Gaussian distributions. The connection leads us to a simple
yet efficient method for sampling from this model, where the basic idea is to
anneal the leakiness rather than the energy; -- i.e., start from a fully
Gaussian/Linear unit and gradually decrease the leakiness over iterations. This
serves as an alternative to the annealing of the temperature parameter and
enables numerical estimation of the likelihood that are more efficient and more
accurate than the commonly used annealed importance sampling (AIS). We further
demonstrate that the proposed sampling algorithm enjoys faster mixing property
than contrastive divergence algorithm, which benefits the training without any
additional computational cost.
| Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos | null | 1611.03879 | null | null |
Unsupervised Learning For Effective User Engagement on Social Media | cs.LG | In this paper, we investigate the effectiveness of unsupervised feature
learning techniques in predicting user engagement on social media.
Specifically, we compare two methods to predict the number of feedbacks (i.e.,
comments) that a blog post is likely to receive. We compare Principal Component
Analysis (PCA) and sparse Autoencoder to a baseline method where the data are
only centered and scaled, on each of two models: Linear Regression and
Regression Tree. We find that unsupervised learning techniques significantly
improve the prediction accuracy on both models. For the Linear Regression
model, sparse Autoencoder achieves the best result, with an improvement in the
root mean squared error (RMSE) on the test set of 42% over the baseline method.
For the Regression Tree model, PCA achieves the best result, with an
improvement in RMSE of 15% over the baseline.
| Thai Pham and Camelia Simoiu | null | 1611.03894 | null | null |
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly
Likelihood | cs.LG stat.ML | We develop a supervised machine learning model that detects anomalies in
systems in real time. Our model processes unbounded streams of data into time
series which then form the basis of a low-latency anomaly detection model.
Moreover, we extend our preliminary goal of just anomaly detection to
simultaneous anomaly prediction. We approach this very challenging problem by
developing a Bayesian Network framework that captures the information about the
parameters of the lagged regressors calibrated in the first part of our
approach and use this structure to learn local conditional probability
distributions.
| Derek Farren and Thai Pham and Marco Alban-Hidalgo | null | 1611.03898 | null | null |
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods | cs.AI cs.LG stat.ML | Reinforcement learning (RL) in Markov decision processes (MDPs) with large
state spaces is a challenging problem. The performance of standard RL
algorithms degrades drastically with the dimensionality of state space.
However, in practice, these large MDPs typically incorporate a latent or hidden
low-dimensional structure. In this paper, we study the setting of
rich-observation Markov decision processes (ROMDP), where there are a small
number of hidden states which possess an injective mapping to the observation
states. In other words, every observation state is generated through a single
hidden state, and this mapping is unknown a priori. We introduce a spectral
decomposition method that consistently learns this mapping, and more
importantly, achieves it with low regret. The estimated mapping is integrated
into an optimistic RL algorithm (UCRL), which operates on the estimated hidden
space. We derive finite-time regret bounds for our algorithm with a weak
dependence on the dimensionality of the observed space. In fact, our algorithm
asymptotically achieves the same average regret as the oracle UCRL algorithm,
which has the knowledge of the mapping from hidden to observed spaces. Thus, we
derive an efficient spectral RL algorithm for ROMDPs.
| Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar | null | 1611.03907 | null | null |
Personalized Donor-Recipient Matching for Organ Transplantation | cs.LG | Organ transplants can improve the life expectancy and quality of life for the
recipient but carries the risk of serious post-operative complications, such as
septic shock and organ rejection. The probability of a successful transplant
depends in a very subtle fashion on compatibility between the donor and the
recipient but current medical practice is short of domain knowledge regarding
the complex nature of recipient-donor compatibility. Hence a data-driven
approach for learning compatibility has the potential for significant
improvements in match quality. This paper proposes a novel system
(ConfidentMatch) that is trained using data from electronic health records.
ConfidentMatch predicts the success of an organ transplant (in terms of the 3
year survival rates) on the basis of clinical and demographic traits of the
donor and recipient. ConfidentMatch captures the heterogeneity of the donor and
recipient traits by optimally dividing the feature space into clusters and
constructing different optimal predictive models to each cluster. The system
controls the complexity of the learned predictive model in a way that allows
for assuring more granular and confident predictions for a larger number of
potential recipient-donor pairs, thereby ensuring that predictions are
"personalized" and tailored to individual characteristics to the finest
possible granularity. Experiments conducted on the UNOS heart transplant
dataset show the superiority of the prognostic value of ConfidentMatch to other
competing benchmarks; ConfidentMatch can provide predictions of success with
95% confidence for 5,489 patients of a total population of 9,620 patients,
which corresponds to 410 more patients than the most competitive benchmark
algorithm (DeepBoost).
| Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, and Mihaela van der
Schaar | null | 1611.03934 | null | null |
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods | cs.LG cs.CR | The problem of anomaly detection has been studied for a long time. In short,
anomalies are abnormal or unlikely things. In financial networks, thieves and
illegal activities are often anomalous in nature. Members of a network want to
detect anomalies as soon as possible to prevent them from harming the network's
community and integrity. Many Machine Learning techniques have been proposed to
deal with this problem; some results appear to be quite promising but there is
no obvious superior method. In this paper, we consider anomaly detection
particular to the Bitcoin transaction network. Our goal is to detect which
users and transactions are the most suspicious; in this case, anomalous
behavior is a proxy for suspicious behavior. To this end, we use three
unsupervised learning methods including k-means clustering, Mahalanobis
distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated
by the Bitcoin transaction network: one graph has users as nodes, and the other
has transactions as nodes.
| Thai Pham and Steven Lee | null | 1611.03941 | null | null |
An Introduction to MM Algorithms for Machine Learning and Statistical | stat.CO cs.LG stat.ML | MM (majorization--minimization) algorithms are an increasingly popular tool
for solving optimization problems in machine learning and statistical
estimation. This article introduces the MM algorithm framework in general and
via three popular example applications: Gaussian mixture regressions,
multinomial logistic regressions, and support vector machines. Specific
algorithms for the three examples are derived and numerical demonstrations are
presented. Theoretical and practical aspects of MM algorithm design are
discussed.
| Hien D. Nguyen | null | 1611.03969 | null | null |
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