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06/22/2023
Context-lumpable stochastic bandits
We consider a contextual bandit problem with S contexts and A actions. I...
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05/24/2023
Regret Matching+: (In)Stability and Fast Convergence in Games
Regret Matching+ (RM+) and its variants are important algorithms for sol...
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02/23/2023
Practical Knowledge Distillation: Using DNNs to Beat DNNs
For tabular data sets, we explore data and model distillation, as well a...
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08/31/2022
Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games
A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled ...
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06/17/2022
Near-Optimal No-Regret Learning for General Convex Games
A recent line of work has established uncoupled learning dynamics such t...
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04/25/2022
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games
In this paper we establish efficient and uncoupled learning dynamics so ...
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02/01/2022
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
While extensive-form games (EFGs) can be converted into normal-form game...
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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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06/27/2021
Last-iterate Convergence in Extensive-Form Games
Regret-based algorithms are highly efficient at finding approximate Nash...
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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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02/08/2021
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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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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02/02/2020
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
We study small-loss bounds for the adversarial multi-armed bandits probl...
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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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11/17/2017