Biography
I am currently building industrial ML systems. I completed my Ph.D. in ECE at UC Davis in Dec 2024, under the supervision of Prof. Lifeng Lai. My research focuses on risk-sensitive reinforcement learning (RL), enhancing algorithmic efficiency, sample complexity, and robustness.I also explored risk-sensitive RL in diverse settings, including reward-free frameworks and with human feedback (RLHF).
I received my Bachelor’s degree in Information Engineering from Zhejiang University, China, in 2019. I collaborated with Prof. Jiangtao Huangfu on integrating deep learning (DL) into medical image diagnosis and autonomous driving.
Publications
- Xinyi Ni and Lifeng Lai. “Provably Efficient Risk-Sensitive Reinforcement Learning with Human Feedback.” IEEE International Symposium on Information Theory (ISIT). 2026. Paper.
- Xinyi Ni and Lifeng Lai. “Risk-Sensitive Reinforcement Learning with $\phi$-Divergence-Risk.” IEEE Transaction on Information Theory (TIT). 2025. Paper
- Xinyi Ni, Guanlin Liu and Lifeng Lai. “Risk-Sensitive Reward-Free Reinforcement Learning with CVaR.” International Conference on Machine Learning (ICML). 2024. Paper
- Xinyi Ni and Lifeng Lai. “Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk.” IEEE Information Theory Workshop (ITW) 2024. Paper
- Xinyi Ni and Lifeng Lai. “Policy Gradient Based Entropic-VaR Optimization in Risk-Sensitive Reinforcement Learning.” Allerton Conference on Communication, Control, and Computing. IEEE, 2022. Paper
- Xinyi Ni and Lifeng Lai. “Risk-sensitive reinforcement learning via Entropic-VaR optimization.” Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022. Paper