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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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08/21/2023
An Improved Best-of-both-worlds Algorithm for Bandits with Delayed Feedback
We propose a new best-of-both-worlds algorithm for bandits with variably...
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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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06/29/2022
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
We present a modified tuning of the algorithm of Zimmert and Seldin [202...
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06/20/2022
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
We revisit the problem of stochastic online learning with feedback graph...
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02/06/2022
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States
We revisit the classical online portfolio selection problem. It is widel...
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10/25/2021
The Pareto Frontier of model selection for general Contextual Bandits
Recent progress in model selection raises the question of the fundamenta...
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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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10/06/2021
Efficient Methods for Online Multiclass Logistic Regression
Multiclass logistic regression is a fundamental task in machine learning...
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07/12/2021
Adapting to Misspecification in Contextual Bandits
A major research direction in contextual bandits is to develop algorithm...
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07/02/2021
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
We provide improved gap-dependent regret bounds for reinforcement learni...
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03/03/2020
Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
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02/27/2020
Online Learning for Active Cache Synchronization
Existing multi-armed bandit (MAB) models make two implicit assumptions: ...
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10/14/2019
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays
We propose a new algorithm for adversarial multi-armed bandits with unre...
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05/28/2019
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
The information-theoretic analysis by Russo and Van Roy (2014) in combin...
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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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07/19/2018
An Optimal Algorithm for Stochastic and Adversarial Bandits
We provide an algorithm that achieves the optimal (up to constants) fini...
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07/04/2018