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 (ICC, 2016)

Tracking the Best Expert in Non-stationary Stochastic Environments. - Chen-Yu Wei; Yi-Te Hong; Chi-Jen Lu (NIPS, 2016)

Online Reinforcement Learning in Stochastic Games. - Chen-Yu Wei; Yi-Te Hong; Chi-Jen Lu (NIPS, 2017)

Efficient Contextual Bandits in Non-stationary Worlds. - Haipeng Luo; Chen-Yu Wei; Alekh Agarwal; John Langford 001 (COLT, 2018)

More Adaptive Algorithms for Adversarial Bandits. - Chen-Yu Wei; Haipeng Luo (COLT, 2018)

Efficient Online Portfolio with Logarithmic Regret. - Haipeng Luo; Chen-Yu Wei; Kai Zheng (NeurIPS, 2018)

Multi-Cell Cooperative Scheduling for Network Utility Maximization With User Equipment Side Interference Cancellation. - Chen-Yu Wei; Wanjiun Liao (IEEE Trans. Wirel. Commun., 2018)

Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. - Peter Auer; Yifang Chen; Pratik Gajane; Chung-Wei Lee; Haipeng Luo; Ronald Ortner; Chen-Yu Wei (COLT, 2019)

Improved Path-length Regret Bounds for Bandits. - Sébastien Bubeck; Yuanzhi Li; Haipeng Luo; Chen-Yu Wei (COLT, 2019)

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)

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. - Alina Beygelzimer; Dávid Pál; Balázs Szörényi; Devanathan Thiruvenkatachari; Chen-Yu Wei; Chicheng Zhang (ICML, 2019)

Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. - Julian Zimmert; Haipeng Luo; Chen-Yu Wei (ICML, 2019)

Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator. - James A. Preiss; Sébastien M. R. Arnold; Chen-Yu Wei; Marius Kloft (CoRR, 2019)

Taking a hint: How to leverage loss predictors in contextual bandits? - Chen-Yu Wei; Haipeng Luo; Alekh Agarwal (COLT, 2020)

Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. - Chen-Yu Wei; Mehdi Jafarnia-Jahromi; Haipeng Luo; Hiteshi Sharma; Rahul Jain 002 (ICML, 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)

Federated Residual Learning. - Alekh Agarwal; John Langford 001; Chen-Yu Wei (CoRR, 2020)

Linear Last-iterate Convergence for Matrix Games and Stochastic Games. - Chung-Wei Lee; Haipeng Luo; Chen-Yu Wei; Mengxiao Zhang (CoRR, 2020)

Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. - Chen-Yu Wei; Mehdi Jafarnia-Jahromi; Haipeng Luo; Rahul Jain 002 (AISTATS, 2021)

Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. - Ehsan Emamjomeh-Zadeh; Chen-Yu Wei; Haipeng Luo; David Kempe 001 (ALT, 2021)

Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition. - Liyu Chen; Haipeng Luo; Chen-Yu Wei (COLT, 2021)

Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications. - Liyu Chen; Haipeng Luo; Chen-Yu Wei (COLT, 2021)

Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach. - Chen-Yu Wei; Haipeng Luo (COLT, 2021)

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)

Linear Last-iterate Convergence in Constrained Saddle-point Optimization. - Chen-Yu Wei; Chung-Wei Lee; Mengxiao Zhang; Haipeng Luo (ICLR, 2021)

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 002 (ICML, 2021)

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses. - Haipeng Luo; Chen-Yu Wei; Chung-Wei Lee (NeurIPS, 2021)

Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure. - Hsu Kao; Chen-Yu Wei; Vijay G. Subramanian (ALT, 2022)

A Model Selection Approach for Corruption Robust Reinforcement Learning. - Chen-Yu Wei; Christoph Dann; Julian Zimmert (ALT, 2022)

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. - Alberto Bietti; Chen-Yu Wei; Miroslav Dudík; John Langford 001; Zhiwei Steven Wu (ICML, 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 R. Jovanovic (ICML, 2022)

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. - Alberto Bietti; Chen-Yu Wei; Miroslav Dudík; John Langford 001; Zhiwei Steven Wu (CoRR, 2022)

A Unified Algorithm for Stochastic Path Problems. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (ALT, 2023)

A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (COLT, 2023)

Refined Regret for Adversarial MDPs with Linear Function Approximation. - Yan Dai 002; Haipeng Luo; Chen-Yu Wei; Julian Zimmert (ICML, 2023)

Best of Both Worlds Policy Optimization. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (ICML, 2023)

Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback. - Yang Cai 001; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (NeurIPS, 2023)

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs. - Dongsheng Ding; Chen-Yu Wei; Kaiqing Zhang; Alejandro Ribeiro (NeurIPS, 2023)

No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions. - Tiancheng Jin; Junyan Liu; Chloé Rouyer; William Chang; Chen-Yu Wei; Haipeng Luo (NeurIPS, 2023)

Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits. - Haolin Liu; Chen-Yu Wei; Julian Zimmert (NeurIPS, 2023)

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. - Julia Olkhovskaya; Jack J. Mayo; Tim van Erven; Gergely Neu; Chen-Yu Wei (NeurIPS, 2023)

Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games. - Yang Cai 001; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (CoRR, 2023)

Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. - Yang Cai 001; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (AISTATS, 2024)

Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. - Zeyu Jia; Alexander Rakhlin; Ayush Sekhari; Chen-Yu Wei (COLT, 2024)

Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. - Haolin Liu; Chen-Yu Wei; Julian Zimmert (ICLR, 2024)

On Tractable Φ-Equilibria in Non-Concave Games. - Yang Cai 001; Constantinos Daskalakis; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (NeurIPS, 2024)

How Does Variance Shape the Regret in Contextual Bandits? - Zeyu Jia; Jian Qian; Alexander Rakhlin; Chen-Yu Wei (NeurIPS, 2024)

Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback. - Haolin Liu; Zakaria Mhammedi; Chen-Yu Wei; Julian Zimmert (NeurIPS, 2024)

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. - Haolin Liu; Artin Tajdini; Andrew Wagenmaker; Chen-Yu Wei (NeurIPS, 2024)

Tractable Local Equilibria in Non-Concave Games. - Yang Cai 001; Constantinos Daskalakis; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (CoRR, 2024)

Decision Making in Hybrid Environments: A Model Aggregation Approach. - Haolin Liu; Chen-Yu Wei; Julian Zimmert (COLT, 2025)

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications. - Yang Cai 001; Haipeng Luo; Chen-Yu Wei; Weiqiang Zheng (CoRR, 2025)