3 citations · 4 across the 5 of their papers we have counts for
5 papers
A Distribution Optimization Framework for Confidence Bounds of Risk Measures
Hao Liang, Zhi-quan Luo
We present a distribution optimization framework that significantly improves confidence bounds for various risk measures compared to previous methods. Our framework encompasses pop…
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
Hao Liang, Zhi-quan Luo
We study finite episodic Markov decision processes incorporating dynamic risk measures to capture risk sensitivity. To this end, we present two model-based algorithms applied to \e…
A Field-Theoretic View of Unlabeled Sensing
Hao Liang, Jingyu Lu, Manolis C. Tsakiris +1
Unlabeled sensing is the problem of solving a linear system of equations, where the right-hand-side vector is known only up to a permutation. In this work, we study fields of ratio…
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds
Hao Liang, Zhi-Quan Luo
We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distributional reinforcement learning (DRL) methods. In particular, we consider finite episodic M…
RareGAN: Generating Samples for Rare Classes
Zinan Lin, Hao Liang, Giulia Fanti +1
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practi…