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Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Chung-Wei Lee*, Haipeng Luo*, Chen-Yu Wei*, Mengxiao Zhang*, Xiaojin Zhang*
ICML 2021
[arXiv]
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Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach
Chen-Yu Wei and Haipeng Luo
COLT 2021 (Best Paper Award)
[arXiv] [slides]
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Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Chen-Yu Wei, Chung-Wei Lee*, Mengxiao Zhang*, Haipeng Luo
COLT 2021
[arXiv] [slides]
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Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
Liyu Chen*, Haipeng Luo*, Chen-Yu Wei*
COLT 2021
[arXiv] [slides]
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Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
Liyu Chen, Haipeng Luo, Chen-Yu Wei
COLT 2021
[arXiv] [slides]
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Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain
AISTAT 2021
[arXiv] [slides]
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Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
ICLR 2021
[arXiv] [code]
[slides]
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Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
Ehsan Emamjomeh-Zadeh*, Chen-Yu Wei*, Haipeng Luo, David Kempe
ALT 2021
[arXiv] [slides]
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Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Chung-Wei Lee*, Haipeng Luo*, Chen-Yu Wei*, Mengxiao Zhang*
NeurIPS 2020 (Oral)
[arXiv] [slides]
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Federated Residual Learning
Chen-Yu Wei, Alekh Agarwal, John Langford
NeurIPS Workshop on Scalability, Privacy, and Security in Federated Learning 2020
[arXiv]
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Taking a Hint: How to Leverage Loss Predictors in Contextual Bandits?
Chen-Yu Wei, Haipeng Luo, Alekh Agarwal
COLT 2020
[arXiv] [slides]
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Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain
ICML 2020
[arXiv]
[code]
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Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
James Preiss*, Sebastien Arnold*, Chen-Yu Wei*, Marius Kloft
NeurIPS Workshop on Optimization Foundations for Reinforcement Learning 2019 [arXiv]
SoCal Machine Learning Symposium 2019 (Best Poster Award)
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A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
Yifang Chen*, Chung-Wei Lee*, Haipeng Luo*, Chen-Yu Wei*
COLT 2019
[arXiv]
[a joint extended abstract with Auer, Gajane, and Ortner] [slides]
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Improved Path-length Regret Bounds for Bandits
Sebastien Bubeck*, Yuanzhi Li*, Haipeng Luo*, Chen-Yu Wei*
COLT 2019
[arXiv] [slides]
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Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer*, David Pal*, Balazs Szorenyi*, Devanathan Thiruvenkatachari*, Chen-Yu Wei*, Chicheng Zhang*
ICML 2019
[arXiv]
[code] [slides]
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Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert, Haipeng Luo, Chen-Yu Wei
ICML 2019 (Long talk)
[arXiv]