
FewShot Learning via Learning the Representation, Provably
This paper studies fewshot learning via representation learning, where ...
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Robust Aggregation for Federated Learning
We present a robust aggregation approach to make federated learning robu...
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Stochastic Gradient Descent Escapes Saddle Points Efficiently
This paper considers the perturbed stochastic gradient descent algorithm...
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A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
In this note, we derive concentration inequalities for random vectors wi...
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Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
Modern deep learning methods provide an effective means to learn good re...
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The Nonstochastic Control Problem
We consider the problem of controlling an unknown linear dynamical syste...
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Optimal Estimation of Change in a Population of Parameters
Paired estimation of change in parameters of interest over a population ...
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The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure
There is a stark disparity between the step size schedules used in pract...
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Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which...
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Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial distu...
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Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
Learning to plan for long horizons is a central challenge in episodic re...
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Maximum Likelihood Estimation for Learning Populations of Parameters
Consider a setting with N independent individuals, each with an unknown ...
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Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Policy gradient methods are among the most effective methods in challeng...
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Provably Efficient Maximum Entropy Exploration
Suppose an agent is in a (possibly unknown) Markov decision process (MDP...
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Calibration, Entropy Rates, and Memory in Language Models
Building accurate language models that capture meaningful longterm depe...
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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
This work provides a simplified proof of the statistical minimax optimal...
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Accelerating Stochastic Gradient Descent
There is widespread sentiment that it is not possible to effectively uti...
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How to Escape Saddle Points Efficiently
This paper shows that a perturbed form of gradient descent converges to ...
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Parallelizing Stochastic Approximation Through MiniBatching and TailAveraging
This work characterizes the benefits of averaging techniques widely used...
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Provable Efficient Online Matrix Completion via Nonconvex Stochastic Gradient Descent
Matrix completion, where we wish to recover a low rank matrix by observi...
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Efficient Algorithms for Largescale Generalized Eigenvector Computation and Canonical Correlation Analysis
This paper considers the problem of canonicalcorrelation analysis (CCA)...
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Streaming PCA: Matching Matrix Bernstein and NearOptimal Finite Sample Guarantees for Oja's Algorithm
This work provides improved guarantees for streaming principle component...
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Unregularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
We develop a family of accelerated stochastic algorithms that minimize s...
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Competing with the Empirical Risk Minimizer in a Single Pass
In many estimation problems, e.g. linear and logistic regression, we wis...
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Planning in POMDPs Using Multiplicity Automata
Planning and learning in Partially Observable MDPs (POMDPs) are among th...
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Least Squares Revisited: Scalable Approaches for Multiclass Prediction
This work provides simple algorithms for multiclass (and multilabel) p...
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A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from obs...
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Analysis of a randomized approximation scheme for matrix multiplication
This note gives a simple analysis of a randomized approximation scheme f...
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Tensor decompositions for learning latent variable models
This work considers a computationally and statistically efficient parame...
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Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental probl...
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Learning mixtures of spherical Gaussians: moment methods and spectral decompositions
This work provides a computationally efficient and statistically consist...
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A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the clu...
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A Method of Moments for Mixture Models and Hidden Markov Models
Mixture models are a fundamental tool in applied statistics and machine ...
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Towards minimax policies for online linear optimization with bandit feedback
We address the online linear optimization problem with bandit feedback. ...
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Spectral Methods for Learning Multivariate Latent Tree Structure
This work considers the problem of learning the structure of multivariat...
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Random design analysis of ridge regression
This work gives a simultaneous analysis of both the ordinary least squar...
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A Risk Comparison of Ordinary Least Squares vs Ridge Regression
We compare the risk of ridge regression to a simple variant of ordinary ...
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Dimensionfree tail inequalities for sums of random matrices
We derive exponential tail inequalities for sums of random matrices with...
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Robust Matrix Decomposition with Outliers
Suppose a given observation matrix can be decomposed as the sum of a low...
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Learning Exponential Families in HighDimensions: Strong Convexity and Sparsity
The versatility of exponential families, along with their attendant conv...
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Regularization Techniques for Learning with Matrices
There is growing body of learning problems for which it is natural to or...
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Global Convergence of Policy Gradient Methods for Linearized Control Problems
Direct policy gradient methods for reinforcement learning and continuous...
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On the insufficiency of existing momentum schemes for Stochastic Optimization
Momentum based stochastic gradient methods such as heavy ball (HB) and N...
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Invariances and Data Augmentation for Supervised Music Transcription
This paper explores a variety of models for framebased music transcript...
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A Smoother Way to Train Structured Prediction Models
We present a framework to train a structured prediction model by perform...
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Sham M. Kakade
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Washington Research Foundation Data Science Chair at University of Washington, Associate Professor in Department of Statistics at University of Washington, Associate Professor in Department of Computer Science at University of Washington, Senior Data Science Fellow, eScience Institute, Adjunct Professor in Department of Electrical Engineering at University of Washington