Chen-Yu Wei
43 papers · 2016–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (9) π Cross-Pollinator (10) π Conference Polyglot (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (26)
πΊοΈ
Taxonomy Completionist
(26)
π§
Keyword Pioneer
π€
Dynamic Duo
(24)
π
Triple Crown
π¬
Deep Specialist
(21)
ποΈ
Keyword Collector
(119)
π
Century Club
(43)
β‘
Prolific Year
(7)
π₯
Unstoppable
(10)
β
The Questioner
(2)
Conferences
NIPS (14)
COLT (13)
ICML (8)
ALT (4)
AISTATS (2)
ICLR (2)
Top co-authors
Research topics
Keywords
regret bound
(23)
online learning
(10)
multi-armed bandit
(8)
contextual bandit
(8)
stochastic optimization
(5)
linear bandit
(5)
dynamic regret
(5)
online algorithm
(4)
adversarial learning
(4)
bandit feedback
(4)
markov decision process
(4)
minimax regret
(4)
markov game
(3)
non-stationary environment
(3)
policy optimization
(3)
adversarial mdp
(3)
nash equilibrium
(3)
linear function approximation
(3)
online mirror descent
(3)
multi-agent system
(3)
Papers
Decision Making in Hybrid Environments: A Model Aggregation Approach
COLT 2025
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
AISTATS 2024
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
NIPS 2024
How Does Variance Shape the Regret in Contextual Bandits?
NIPS 2024
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
COLT 2024
On Tractable $\Phi$-Equilibria in Non-Concave Games
NIPS 2024
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
ICLR 2024
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
NIPS 2024
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
NIPS 2023
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
NIPS 2023
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
NIPS 2023
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
NIPS 2023
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
NIPS 2023
A Unified Algorithm for Stochastic Path Problems
ALT 2023
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
COLT 2023
Refined Regret for Adversarial MDPs with Linear Function Approximation
ICML 2023
Best of Both Worlds Policy Optimization
ICML 2023
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure
ALT 2022
A Model Selection Approach for Corruption Robust Reinforcement Learning
ALT 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
ICML 2022
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
ICML 2022
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
ICML 2021
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
AISTATS 2021
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
COLT 2021
Impossible Tuning Made Possible: A New Expert Algorithm and Its Applications
COLT 2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
COLT 2021
Non-stationary Reinforcement Learning without Prior Knowledge: an Optimal Black-box Approach
COLT 2021
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
ALT 2021
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
ICLR 2021
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
NIPS 2021
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
ICML 2020
Taking a hint: How to leverage loss predictors in contextual bandits?
COLT 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
NIPS 2020
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
ICML 2019
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
ICML 2019
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information
COLT 2019
Improved Path-length Regret Bounds for Bandits
COLT 2019
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal and Parameter-free
COLT 2019
More Adaptive Algorithms for Adversarial Bandits
COLT 2018
Efficient Online Portfolio with Logarithmic Regret
NIPS 2018
Efficient Contextual Bandits in Non-stationary Worlds
COLT 2018
Online Reinforcement Learning in Stochastic Games
NIPS 2017
Tracking the Best Expert in Non-stationary Stochastic Environments
NIPS 2016