Sham M. Kakade

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Microsoft ∙ University of Pennsylvania ∙ University of Washington

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

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Few-Shot Learning via Learning the Representation, Provably

This paper studies few-shot learning via representation learning, where ...
02/21/2020 ∙ by Simon S. Du, et al. ∙ 46 ∙ share

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Robust Aggregation for Federated Learning

We present a robust aggregation approach to make federated learning robu...
12/31/2019 ∙ by Krishna Pillutla, et al. ∙ 22 ∙ share

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Stochastic Gradient Descent Escapes Saddle Points Efficiently

This paper considers the perturbed stochastic gradient descent algorithm...
02/13/2019 ∙ by Chi Jin, et al. ∙ 20 ∙ share

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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...
02/11/2019 ∙ by Chi Jin, et al. ∙ 16 ∙ share

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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...
10/07/2019 ∙ by Simon S. Du, et al. ∙ 15 ∙ share

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The Nonstochastic Control Problem

We consider the problem of controlling an unknown linear dynamical syste...
11/27/2019 ∙ by Elad Hazan, et al. ∙ 15 ∙ share

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Optimal Estimation of Change in a Population of Parameters

Paired estimation of change in parameters of interest over a population ...
11/28/2019 ∙ by Ramya Korlakai Vinayak, et al. ∙ 15 ∙ share

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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...
04/29/2019 ∙ by Rong Ge, et al. ∙ 12 ∙ share

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Coupled Recurrent Models for Polyphonic Music Composition

This work describes a novel recurrent model for music composition, which...
11/20/2018 ∙ by John Thickstun, et al. ∙ 10 ∙ share

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Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...
02/23/2019 ∙ by Naman Agarwal, et al. ∙ 8 ∙ share

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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...
05/01/2020 ∙ by Ruosong Wang, et al. ∙ 7 ∙ share

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Maximum Likelihood Estimation for Learning Populations of Parameters

Consider a setting with N independent individuals, each with an unknown ...
02/12/2019 ∙ by Ramya Korlakai Vinayak, et al. ∙ 4 ∙ share

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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...
08/01/2019 ∙ by Alekh Agarwal, et al. ∙ 3 ∙ share

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Provably Efficient Maximum Entropy Exploration

Suppose an agent is in a (possibly unknown) Markov decision process (MDP...
12/06/2018 ∙ by Elad Hazan, et al. ∙ 2 ∙ share

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Calibration, Entropy Rates, and Memory in Language Models

Building accurate language models that capture meaningful long-term depe...
06/11/2019 ∙ by Mark Braverman, et al. ∙ 1 ∙ share

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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...
10/25/2017 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Accelerating Stochastic Gradient Descent

There is widespread sentiment that it is not possible to effectively uti...
04/26/2017 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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How to Escape Saddle Points Efficiently

This paper shows that a perturbed form of gradient descent converges to ...
03/02/2017 ∙ by Chi Jin, et al. ∙ 0 ∙ share

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Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging

This work characterizes the benefits of averaging techniques widely used...
10/12/2016 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent

Matrix completion, where we wish to recover a low rank matrix by observi...
05/26/2016 ∙ by Chi Jin, et al. ∙ 0 ∙ share

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Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

This paper considers the problem of canonical-correlation analysis (CCA)...
04/13/2016 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm

This work provides improved guarantees for streaming principle component...
02/22/2016 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

We develop a family of accelerated stochastic algorithms that minimize s...
06/24/2015 ∙ by Roy Frostig, et al. ∙ 0 ∙ share

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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...
12/20/2014 ∙ by Roy Frostig, et al. ∙ 0 ∙ share

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Planning in POMDPs Using Multiplicity Automata

Planning and learning in Partially Observable MDPs (POMDPs) are among th...
07/04/2012 ∙ by Eyal Even-Dar, et al. ∙ 0 ∙ share

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Least Squares Revisited: Scalable Approaches for Multi-class Prediction

This work provides simple algorithms for multi-class (and multi-label) p...
10/07/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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A Tensor Approach to Learning Mixed Membership Community Models

Community detection is the task of detecting hidden communities from obs...
02/12/2013 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ share

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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...
11/23/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Tensor decompositions for learning latent variable models

This work considers a computationally and statistically efficient parame...
10/29/2012 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ share

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Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints

Unsupervised estimation of latent variable models is a fundamental probl...
09/24/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Learning mixtures of spherical Gaussians: moment methods and spectral decompositions

This work provides a computationally efficient and statistically consist...
06/25/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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...
04/30/2012 ∙ by Dean P. Foster, et al. ∙ 0 ∙ share

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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 ...
03/03/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Towards minimax policies for online linear optimization with bandit feedback

We address the online linear optimization problem with bandit feedback. ...
02/14/2012 ∙ by Sébastien Bubeck, et al. ∙ 0 ∙ share

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Spectral Methods for Learning Multivariate Latent Tree Structure

This work considers the problem of learning the structure of multivariat...
07/07/2011 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ share

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Random design analysis of ridge regression

This work gives a simultaneous analysis of both the ordinary least squar...
06/13/2011 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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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 ...
05/04/2011 ∙ by Paramveer S. Dhillon, et al. ∙ 0 ∙ share

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Dimension-free tail inequalities for sums of random matrices

We derive exponential tail inequalities for sums of random matrices with...
04/09/2011 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Robust Matrix Decomposition with Outliers

Suppose a given observation matrix can be decomposed as the sum of a low...
11/05/2010 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

The versatility of exponential families, along with their attendant conv...
10/31/2009 ∙ by Sham M. Kakade, et al. ∙ 0 ∙ share

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Regularization Techniques for Learning with Matrices

There is growing body of learning problems for which it is natural to or...
10/04/2009 ∙ by Sham M. Kakade, et al. ∙ 0 ∙ share

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Global Convergence of Policy Gradient Methods for Linearized Control Problems

Direct policy gradient methods for reinforcement learning and continuous...
01/15/2018 ∙ by Maryam Fazel, et al. ∙ 0 ∙ share

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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...
03/15/2018 ∙ by Rahul Kidambi, et al. ∙ 0 ∙ share

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Invariances and Data Augmentation for Supervised Music Transcription

This paper explores a variety of models for frame-based music transcript...
11/13/2017 ∙ by John Thickstun, et al. ∙ 0 ∙ share

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A Smoother Way to Train Structured Prediction Models

We present a framework to train a structured prediction model by perform...
02/08/2019 ∙ by Krishna Pillutla, et al. ∙ 0 ∙ share

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