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Hao Liang is a Research Associate at King's College London. He received his PhD from The Chinese University of Hong Kong (CUHK), Shenzhen, under the supervision of Zhi-Quan (Tom) Luo. His research lies at the intersection of statistical and computational efficiency in decision-making algorithms, with a particular focus on risk awareness, safety, and multi-agent systems. He is also interested in exploring the applications of these algorithms in various domains, including generative AI, microgrids, and wireless communications.
Email: haoliang1 at link.cuhk.edu.cn [CV] [Google Scholar] [LinkedIn] |
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News
August 2024: Our paper, "An Economic and Low-carbon Dispatch Algorithm for Microgrids with Electric Vehicles" is awarded the best oral presentation at PSET 2024
July 2024: My paper, "Bridging Distributional and Risk-sensitive Reinforcement Learning with
Provable Regret Bounds" is accepted by JMLR
July 2024: Present "Bridging Distributional and Risk-Sensitive Reinforcement Learning: Balancing Statistical,
Computational, and Risk Considerations" at ICML 2024 FoRLaC Workshop
June 2024: Our paper, "An Economic and Low-carbon Dispatch Algorithm for Microgrids with Electric Vehicles" is accepted for oral presentation at PSET 2024
May 2024: My paper, "Bridging Distributional and Risk-sensitive Reinforcement Learning with
Provable Regret Bounds" is accepted with minor revision at JMLR
March 2024: Deliver a talk "Efficient Risk-aware Decision-making: A Distributional Perspective" at Vector Institute
March 2024: Deliver a talk "A Distribution Optimization Framework for Confidence Bounds of Risk
Measures" at the Informs Optimization Society (IOS) Conference
Janurary 2024: My paper, "Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz
Dynamic Risk Measures" is accepted at AISTATS 2024
July 2023: Present "A Distribution Optimization Framework for Confidence Bounds of Risk
Measures" at ICML 2023
Selected Publications
| Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds [link] | JMLR |
| Hao Liang, Zhi-Quan Luo | |
| Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures [link] | AISTATS 2024 |
| Hao Liang, Zhi-Quan Luo | |
| A Distribution Optimization Framework for Confidence Bounds of Risk Measures [link] | ICML 2023 |
| Hao Liang, Zhi-Quan Luo | |