| Bridging Distributional and Risk-Sensitive Reinforcement Learning: Balancing Statistical, Computational, and Risk Considerations [link] |
| Hao Liang |
| Present at ICML 2024 Workshop on FoRLaC |
| Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds [link] | JMLR |
| Hao Liang, Zhi-Quan Luo | |
| An earlier version presented at NeurIPS 2021 Workshop on Ecological Theory of RL | |
| An Economic and Low-carbon Dispatch Algorithm for Microgrids with Electric Vehicles | PSET 2024 |
| Jiayu Cheng, Hao Liang, Xiaoying Tang, Shuguang Cui | |
| Best oral presentation | |
| Optimistic Thompson Sampling for No-regret Learning in Unknown Games [arXiv] |
| Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang, Zhi-Quan Luo |
| Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures [AISTATS] | AISTATS 2024 |
| Hao Liang, Zhi-Quan Luo | |
| An earlier version presented at ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems | |
| A Distribution Optimization Framework for Confidence Bounds of Risk Measures [ICML] | ICML 2023 |
| Hao Liang, Zhi-Quan Luo | |
| Is Pure Exploitation Sufficient for Sequential Decision-Making with Exogenous Information? |
| Hao Liang |
| Revisiting Minimax Lower Bounds in Unknown Matrix Game |
| Hao Liang, Yingru Li, Zhi-Quan Luo |
| A Convergence Analysis of Categorical Distributional Reinforcement Learning Algorithm |
| Hao Liang, Zhi-Quan Luo |