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09/02/2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
We consider the adversarial linear contextual bandit problem, where the ...
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06/20/2023
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
We study the problem of computing an optimal policy of an infinite-horiz...
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05/27/2023
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Existing online learning algorithms for adversarial Markov Decision Proc...
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05/01/2023
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
We consider the adversarial linear contextual bandit setting, which allo...
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03/05/2023
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games
We revisit the problem of learning in two-player zero-sum Markov games, ...
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02/20/2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
Best-of-both-worlds algorithms for online learning which achieve near-op...
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02/18/2023
Best of Both Worlds Policy Optimization
Policy optimization methods are popular reinforcement learning algorithm...
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10/17/2022
A Unified Algorithm for Stochastic Path Problems
We study reinforcement learning in stochastic path (SP) problems. The go...
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02/10/2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
Large-scale machine learning systems often involve data distributed acro...
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02/08/2022
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
We examine global non-asymptotic convergence properties of policy gradie...
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11/01/2021
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure
Multi-agent reinforcement learning (MARL) problems are challenging due t...
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10/07/2021
A Model Selection Approach for Corruption Robust Reinforcement Learning
We develop a model selection approach to tackle reinforcement learning w...
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07/18/2021
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
Policy optimization is a widely-used method in reinforcement learning. D...
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02/11/2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
In this work, we develop linear bandit algorithms that automatically ada...
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02/10/2021
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach
We propose a black-box reduction that turns a certain reinforcement lear...
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02/08/2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
We study infinite-horizon discounted two-player zero-sum Markov games, a...
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02/01/2021
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
We resolve the long-standing "impossible tuning" issue for the classic e...
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12/07/2020
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
We study the stochastic shortest path problem with adversarial costs and...
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07/23/2020
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
We develop several new algorithms for learning Markov Decision Processes...
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06/16/2020
Linear Last-iterate Convergence for Matrix Games and Stochastic Games
Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...
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06/14/2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
We develop a new approach to obtaining high probability regret bounds fo...
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06/08/2020
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret
Recently, model-free reinforcement learning has attracted research atten...
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03/28/2020
Federated Residual Learning
We study a new form of federated learning where the clients train person...
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03/07/2020
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
We revisit the problem of online learning with sleeping experts/bandits:...
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03/04/2020
Taking a hint: How to leverage loss predictors in contextual bandits?
We initiate the study of learning in contextual bandits with the help of...
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10/15/2019
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Model-free reinforcement learning is known to be memory and computation ...
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10/02/2019
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
We study the variance of the REINFORCE policy gradient estimator in envi...
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02/06/2019
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
We study the problem of efficient online multiclass linear classificatio...
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02/03/2019
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
We propose the first contextual bandit algorithm that is parameter-free,...
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01/29/2019
Improved Path-length Regret Bounds for Bandits
We study adaptive regret bounds in terms of the variation of the losses ...
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01/25/2019
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
We develop the first general semi-bandit algorithm that simultaneously a...
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05/18/2018
Efficient Online Portfolio with Logarithmic Regret
We study the decades-old problem of online portfolio management and prop...
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01/10/2018