|
||||||||
Research Interest My recent research interest generally focuses on solving the decision-making problems. Specifically, I am interested in reinforcement learning and games, optimization, and statistical learning from the theoretical perspective. In addition, I am also interested in the application of machine learning to the various areas such as econometrics, computer vision, and data mining.
|
||||||||
Selected Recent Publication [Full List]
Posterior Sampling for Competitive RL: Function Approximation
and Partial Observation
Advances in Neural Information Processing Systems (NeurIPS), 2023 [PDF] Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics International Conference on Learning Representations (ICLR), 2023 [PDF] Gradient-Variation Bound for Online Convex Optimization with Constraints AAAI Conference on Artificial Intelligence (AAAI), 2023 [PDF] Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning International Conference on Machine Learning (ICML), 2022 [PDF] [Codes] In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle International Conference on Management of Data (SIGMOD), 2022 [PDF] [Extended Version] On
Reward-Free RL with Kernel and Neural Function Approximations:
Single-Agent MDP and Markov Game
International Conference on Machine Learning (ICML), 2021 [PDF]
Provably Efficient Fictitious Play Policy Optimization for
Zero-Sum Markov Games with Structured Transitions
International Conference on Machine Learning (ICML), 2021 [PDF] Stylized Neural Painting IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Codes] [Project]
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss Advances in Neural Information Processing Systems (NeurIPS), 2020 [PDF]
Robust One-Bit Recovery via ReLU Generative Networks:
Near-Optimal Statistical Rate and Global Landscape Analysis
International Conference on Machine Learning (ICML), 2020 [PDF] |
||||||||
Academic Service Conference
|