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2020 – today
- 2025
[i43]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Decision Making in Hybrid Environments: A Model Aggregation Approach. CoRR abs/2502.05974 (2025)- 2024
- [c43]Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. AISTATS 2024: 3889-3897 - [c42]Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. COLT 2024: 2644-2719 - [c41]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. ICLR 2024 - [c40]Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
On Tractable Φ-Equilibria in Non-Concave Games. NeurIPS 2024 - [c39]Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei:
How Does Variance Shape the Regret in Contextual Bandits? NeurIPS 2024 - [c38]Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei, Julian Zimmert:
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback. NeurIPS 2024 - [c37]Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei:
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. NeurIPS 2024 - [i42]Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. CoRR abs/2401.15240 (2024) - [i41]Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Tractable Local Equilibria in Non-Concave Games. CoRR abs/2403.08171 (2024) - [i40]Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. CoRR abs/2403.17091 (2024) - [i39]Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei:
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. CoRR abs/2410.07533 (2024) - [i38]Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei:
How Does Variance Shape the Regret in Contextual Bandits? CoRR abs/2410.12713 (2024) - [i37]Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei, Julian Zimmert:
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback. CoRR abs/2411.06739 (2024) - 2023
- [c36]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. ALT 2023: 510-557 - [c35]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. COLT 2023: 5503-5570 - [c34]Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. ICML 2023: 6726-6759 - [c33]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. ICML 2023: 6968-7008 - [c32]Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback. NeurIPS 2023 - [c31]Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Alejandro Ribeiro:
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs. NeurIPS 2023 - [c30]Tiancheng Jin, Junyan Liu, Chloé Rouyer, William Chang, Chen-Yu Wei, Haipeng Luo:
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions. NeurIPS 2023 - [c29]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits. NeurIPS 2023 - [c28]Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS 2023 - [i36]Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert:
Refined Regret for Adversarial MDPs with Linear Function Approximation. CoRR abs/2301.12942 (2023) - [i35]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. CoRR abs/2302.09408 (2023) - [i34]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. CoRR abs/2302.09739 (2023) - [i33]Yang Cai
, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games. CoRR abs/2303.02738 (2023) - [i32]Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR abs/2305.00832 (2023) - [i31]Tiancheng Jin, Junyan Liu, Chloé Rouyer, William Chang, Chen-Yu Wei, Haipeng Luo:
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions. CoRR abs/2305.17380 (2023) - [i30]Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Alejandro Ribeiro:
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs. CoRR abs/2306.11700 (2023) - [i29]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits. CoRR abs/2309.00814 (2023) - [i28]Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback. CoRR abs/2310.11550 (2023) - 2022
- [c27]Hsu Kao, Chen-Yu Wei, Vijay G. Subramanian:
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure. ALT 2022: 573-605 - [c26]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. ALT 2022: 1043-1096 - [c25]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c24]Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo R. Jovanovic:
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence. ICML 2022: 5166-5220 - [i27]Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo R. Jovanovic:
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence. CoRR abs/2202.04129 (2022) - [i26]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR abs/2202.05318 (2022) - [i25]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. CoRR abs/2210.09255 (2022) - 2021
- [c23]Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain:
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS 2021: 3007-3015 - [c22]Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe:
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. ALT 2021: 599-618 - [c21]Liyu Chen, Haipeng Luo, Chen-Yu Wei:
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition. COLT 2021: 1180-1215 - [c20]Liyu Chen, Haipeng Luo, Chen-Yu Wei:
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications. COLT 2021: 1216-1259 - [c19]Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo:
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games. COLT 2021: 4259-4299 - [c18]Chen-Yu Wei, Haipeng Luo:
Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach. COLT 2021: 4300-4354 - [c17]Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo:
Linear Last-iterate Convergence in Constrained Saddle-point Optimization. ICLR 2021 - [c16]Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang:
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously. ICML 2021: 6142-6151 - [c15]Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee:
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses. NeurIPS 2021: 22931-22942 - [i24]Liyu Chen, Haipeng Luo, Chen-Yu Wei:
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications. CoRR abs/2102.01046 (2021) - [i23]Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo:
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games. CoRR abs/2102.04540 (2021) - [i22]Chen-Yu Wei, Haipeng Luo:
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach. CoRR abs/2102.05406 (2021) - [i21]Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang:
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously. CoRR abs/2102.05858 (2021) - [i20]Haipeng Luo, Chen-Yu Wei, Chung-Wei Lee:
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses. CoRR abs/2107.08346 (2021) - [i19]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. CoRR abs/2110.03580 (2021) - [i18]Hsu Kao, Chen-Yu Wei, Vijay G. Subramanian:
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure. CoRR abs/2111.00781 (2021) - 2020
- [c14]Chen-Yu Wei, Haipeng Luo, Alekh Agarwal:
Taking a hint: How to leverage loss predictors in contextual bandits? COLT 2020: 3583-3634 - [c13]Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain:
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. ICML 2020: 10170-10180 - [c12]Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang:
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs. NeurIPS 2020 - [i17]Chen-Yu Wei, Haipeng Luo, Alekh Agarwal:
Taking a hint: How to leverage loss predictors in contextual bandits? CoRR abs/2003.01922 (2020) - [i16]Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo, David Kempe:
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds. CoRR abs/2003.03490 (2020) - [i15]Alekh Agarwal, John Langford, Chen-Yu Wei:
Federated Residual Learning. CoRR abs/2003.12880 (2020) - [i14]Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang:
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs. CoRR abs/2006.08040 (2020) - [i13]Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang:
Linear Last-iterate Convergence for Matrix Games and Stochastic Games. CoRR abs/2006.09517 (2020) - [i12]Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain:
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR abs/2007.11849 (2020) - [i11]Liyu Chen, Haipeng Luo, Chen-Yu Wei:
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition. CoRR abs/2012.04053 (2020)
2010 – 2019
- 2019
- [c11]Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei:
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. COLT 2019: 159-163 - [c10]Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei:
Improved Path-length Regret Bounds for Bandits. COLT 2019: 508-528 - [c9]Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei:
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal and Parameter-free. COLT 2019: 696-726 - [c8]Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang:
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. ICML 2019: 624-633 - [c7]Julian Zimmert, Haipeng Luo, Chen-Yu Wei:
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. ICML 2019: 7683-7692 - [i10]Julian Zimmert, Haipeng Luo, Chen-Yu Wei:
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. CoRR abs/1901.08779 (2019) - [i9]Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei:
Improved Path-length Regret Bounds for Bandits. CoRR abs/1901.10604 (2019) - [i8]Yifang Chen, Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei:
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free. CoRR abs/1902.00980 (2019) - [i7]Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang:
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. CoRR abs/1902.02244 (2019) - [i6]James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft:
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator. CoRR abs/1910.01249 (2019) - [i5]Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain:
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. CoRR abs/1910.07072 (2019) - 2018
- [j1]Chen-Yu Wei, Wanjiun Liao:
Multi-Cell Cooperative Scheduling for Network Utility Maximization With User Equipment Side Interference Cancellation. IEEE Trans. Wirel. Commun. 17(1): 619-635 (2018) - [c6]Chen-Yu Wei, Haipeng Luo:
More Adaptive Algorithms for Adversarial Bandits. COLT 2018: 1263-1291 - [c5]Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. COLT 2018: 1739-1776 - [c4]Haipeng Luo, Chen-Yu Wei, Kai Zheng:
Efficient Online Portfolio with Logarithmic Regret. NeurIPS 2018: 8245-8255 - [i4]Chen-Yu Wei, Haipeng Luo:
More Adaptive Algorithms for Adversarial Bandits. CoRR abs/1801.03265 (2018) - [i3]Haipeng Luo, Chen-Yu Wei, Kai Zheng:
Efficient Online Portfolio with Logarithmic Regret. CoRR abs/1805.07430 (2018) - 2017
- [c3]Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu:
Online Reinforcement Learning in Stochastic Games. NIPS 2017: 4987-4997 - [i2]Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu:
Tracking the Best Expert in Non-stationary Stochastic Environments. CoRR abs/1712.00578 (2017) - [i1]Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu:
Online Reinforcement Learning in Stochastic Games. CoRR abs/1712.00579 (2017) - 2016
- [c2]Hsu Kao, Chen-Yu Wei, Hsiao-Ching Lin, Yi-Han Chiang, Wanjiun Liao:
Adaptive measurement for energy efficient mobility management in ultra-dense small cell networks. ICC 2016: 1-6 - [c1]Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu:
Tracking the Best Expert in Non-stationary Stochastic Environments. NIPS 2016: 3972-3980
Coauthor Index
Chung-wei Lee
aka: Chung-Wei Lee
aka: Chung-Wei Lee
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