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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...
02/11/2021 ∙ by Chung-Wei Lee, et al. ∙
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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...
02/08/2021 ∙ by Chen-Yu Wei, et al. ∙
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Linear Last-iterate Convergence for Matrix Games and Stochastic Games
Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...
06/16/2020 ∙ by Chung-Wei Lee, et al. ∙
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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...
06/14/2020 ∙ by Chung-Wei Lee, et al. ∙
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A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
We study small-loss bounds for the adversarial multi-armed bandits probl...
02/02/2020 ∙ by Chung-Wei Lee, et al. ∙
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A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
We propose the first contextual bandit algorithm that is parameter-free,...
02/03/2019 ∙ by Yifang Chen, et al. ∙
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Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-l...
11/17/2017 ∙ by Chung-Wei Lee, et al. ∙
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