
PCPG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Direct policy gradient methods for reinforcement learning are a successf...
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Information Theoretic Regret Bounds for Online Nonlinear Control
This work studies the problem of sequential control in an unknown, nonli...
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FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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Robust Metalearning for Mixed Linear Regression with Small Batches
A common challenge faced in practical supervised learning, such as medic...
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PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
The global health threat from COVID19 has been controlled in a number o...
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Optimal Regularization Can Mitigate Double Descent
Recent empirical and theoretical studies have shown that many learning a...
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The Implicit and Explicit Regularization Effects of Dropout
Dropout is a widelyused regularization technique, often required to obt...
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Provable Representation Learning for Imitation Learning via Bilevel Optimization
A common strategy in modern learning systems is to learn a representatio...
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Metalearning for mixed linear regression
In modern supervised learning, there are a large number of tasks, but ma...
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Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
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MetaLearning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn...
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On the Optimality of Sparse ModelBased Planning for Markov Decision Processes
This work considers the sample complexity of obtaining an ϵoptimal poli...
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Online MetaLearning
A central capability of intelligent systems is the ability to continuous...
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Plan Online, Learn Offline: Efficient Learning and Exploration via ModelBased Control
We propose a plan online and learn offline (POLO) framework for the sett...
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Provably Correct Automatic Subdifferentiation for Qualified Programs
The Cheap Gradient Principle (Griewank 2008)  the computational cost ...
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Stochastic subgradient method converges on tame functions
This work considers the question: what convergence guarantees does the s...
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Variance Reduction for Policy Gradient with ActionDependent Factorized Baselines
Policy gradient methods have enjoyed great success in deep reinforcement...
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Variance Reduction Methods for Sublinear Reinforcement Learning
This work considers the problem of provably optimal reinforcement learni...
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Leverage Score Sampling for Faster Accelerated Regression and ERM
Given a matrix A∈R^n× d and a vector b ∈R^d, we show how to compute an ϵ...
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Learning Overcomplete HMMs
We study the problem of learning overcomplete HMMsthose that have man...
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Prediction with a Short Memory
We consider the problem of predicting the next observation given a seque...
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Convergence Rates of Active Learning for Maximum Likelihood Estimation
An active learner is given a class of models, a large set of unlabeled e...
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A Linear Dynamical System Model for Text
Low dimensional representations of words allow accurate NLP models to be...
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When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
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(weak) Calibration is Computationally Hard
We show that the existence of a computationally efficient calibration al...
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An Optimal Algorithm for Linear Bandits
We provide the first algorithm for online bandit linear optimization who...
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Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide ...
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Learning from Logged Implicit Exploration Data
We provide a sound and consistent foundation for the use of nonrandom ex...
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Sham Kakade
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Washington Research Foundation Data Science Chair, with a joint appointment in both the Computer Science & Engineering and Statistics departments at the University of Washington.