About Me

Hi! My name is Yu Chen (陈禹 in Chinese).

I am a third-year Ph.D. student at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, where I am very fortunate to be advised by Prof. Longbo Huang. My research focuses on reinforcement learning theory, with a particular interest in robust sequential decision-making under uncertainty.

I had the great opportunity to work as a research intern at MSR Asia Theory Center from February to August 2024, under the guidance of Dr. Wei Chen.

Prior to my Ph.D., I received my Bachelor of Science from Tsinghua University in Mathematics.

Research Interests

My research focuses on the theoretical foundations of reinforcement learning and online decision-making. My current interests can be broadly organized into three directions:

• Heavy-tailed feedback and robust online decision-making: designing algorithms that learn reliably from noisy, unbounded, or heavy-tailed feedback, with guarantees that adapt to both benign stochastic environments and more challenging adversarial regimes. Representative works include BoBW Parameter-Free Heavy-Tailed MABs, BoBW Heavy-Tailed MDPs, and Continuous K-Max Bandits.

• Risk-sensitive reinforcement learning: studying reinforcement learning beyond the standard expected-return objective, especially when the learner must optimize tail performance, distributional criteria, or other risk-aware notions of long-term reward. Representative works include Distributional RL for Lipschitz Risk, Iterated CVaR RL, and Risk-Sensitive POMDPs.

• Reinforcement learning with function approximation: developing theory for large state-action spaces beyond the tabular setting, where function classes are used to generalize across states and actions while preserving sample-efficiency and finite-time guarantees. Representative works include Reward-Free Linear RL, General Function Approximation, and Optimal Linear RL.

I am also broadly interested in scheduling problems, decision problem in large language models, and diffusion theory. I am always open to new ideas and collaborations, so please feel free to reach out if you would like to discuss related topics.

Academic Services

• Conference Reviewing: NeurIPS; ICLR; ICML;
• Journal Reviewing: Expert Systems With Applications;