Published Papers (* indicates equal contribution or alphabetical ordering)

2022

• Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
  Dongsheng Ding*, Chen-Yu Wei*, Kaiqing Zhang*, Mihailo Jovanovic
  ICML 2022 (Long Presentation) [arXiv]

• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization
  Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, Zhiwei Steven Wu
  ICML 2022 [arXiv]

• Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure
  Hsu Kao, Chen-Yu Wei, Vijay Subramanian
  ALT 2022 [arXiv] [slides]

• A Model Selection Approach for Corruption Robust Reinforcement Learning
  Chen-Yu Wei, Christoph Dann, Julian Zimmert
  ALT 2022 (Best Paper Award) [arXiv] [slides]

2021

• Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
  Haipeng Luo*, Chen-Yu Wei*, Chung-Wei Lee
  NeurIPS 2021 [arXiv] [slides]

• 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] [slides]

• 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]

• 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]

• Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
  Liyu Chen*, Haipeng Luo*, Chen-Yu Wei*
  COLT 2021 [arXiv] [slides]

• Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
  Liyu Chen, Haipeng Luo, Chen-Yu Wei
  COLT 2021 [arXiv] [slides]

• Learning Infinite-Horizon Average-Reward MDPs with Linear Function Approximation
  Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain
  AISTAT 2021 [arXiv] [slides]

• Linear Last-Iterate Convergence in Constrained Saddle-Point Optimization
  Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo
  ICLR 2021 [arXiv] [code] [slides]

• 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]

2020

• 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]

• Federated Residual Learning
  Chen-Yu Wei, Alekh Agarwal, John Langford
  NeurIPS Workshop on Scalability, Privacy, and Security in Federated Learning 2020 [arXiv] [workshop version]

• Taking a Hint: How to Leverage Loss Predictors in Contextual Bandits?
  Chen-Yu Wei, Haipeng Luo, Alekh Agarwal
  COLT 2020 [arXiv] [slides]

• 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] [slides]

2019

• 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)

• 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]

• Improved Path-Length Regret Bounds for Bandits
  Sebastien Bubeck*, Yuanzhi Li*, Haipeng Luo*, Chen-Yu Wei*
  COLT 2019 [arXiv] [slides]

• 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]

• Beating Stochastic and Adversarial Semi-Bandits Optimally and Simultaneously
  Julian Zimmert, Haipeng Luo, Chen-Yu Wei
  ICML 2019 (Long Presentation) [arXiv] [slides]

2018

• Efficient Online Portfolio with Logarithmic Regret
  Haipeng Luo*, Chen-Yu Wei*, Kai Zheng*
  NeurIPS 2018 (Spotlight) [arXiv] [slides]

• Efficient Contextual Bandits in Non-Stationary Worlds
  Haipeng Luo*, Chen-Yu Wei*, Alekh Agarwal, John Langford
  COLT 2018 [arXiv] [slides]

• More Adaptive Algorithms for Adversarial Bandits
  Chen-Yu Wei and Haipeng Luo
  COLT 2018 [arXiv] [slides]

• Multi-Cell Cooperative Scheduling for Network Utility Maximization with User Equipment Side Interference Cancellation
  Chen-Yu Wei and Wanjiun Liao
  IEEE Transactions on Wireless Communications 2018 [IEEE Xplore]

Before 2018

• Online Reinforcement Learning in Stochastic Games
  Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
  NeurIPS 2017 [arXiv]

• Tracking the Best Expert in Non-Stationary Stochastic Environments
  Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
  NeurIPS 2016 [arXiv]

• Adaptive Measurement for Energy Efficient Mobility Management in Ultra-Dense Small Cell Networks
  Hsu Kao, Chen-Yu Wei, Hsiao-Ching Lin, Yi-Han Chiang, Wanjiun Liao
  International Conference on Communications 2016 [IEEE Xplore]