I am a fourth-year PhD student at the University of Alberta, supervised by Dr. A. Rupam Mahmood.

My primary research interest focuses on effectively using data, especially for reinforcement learning. I design and analyze algorithms that leverage datasets experiencing distribution shifts and investigate how data should be collected. I am also interested in understanding more about transformers, including their representation power and in-context learning abilities.

My research philosophy centers on understanding empirical problems from a theoretical perspective and guiding algorithm design with theoretical insights. I can run experiments, and I can do proofs.

News

2025.02: Our tutorial “Advancing Offline Reinforcement Learning: Essential Theories and Techniques for Algorithm Developers” is accepted at AAAI 2025, Philadelphia, PA, hosted by Fengdi Che, Chenjun Xiao, Ming Yin, and Csaba Szepesvári!

2024.12: I will attend NeurIPS 2024 in Vancouver. Wish to chat with you if you are interested in my research topics!

2024.07: Our paper “Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation” is presented at ICML 24 as a spotlight paper with only 3.5% acceptance rate.

Publications

Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A Ramirez, Christopher K Harris, A Rupam Mahmood, Dale Schuurmans. Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. ICML 2024 (spotlight) (Acceptance Rate 3.5%).

Fengdi Che, Gautham Vasan, Rupam Mahmood. Correcting Discount-Factor Mismatch in On-Policy Policy Gradient Methods. ICML 2023 (Acceptance Rate 27.9%).

Jiamin He, Fengdi Che, Yi Wan, Rupam Mahmood. Consistent Emphatic Temporal-Difference Learning. UAI 2023 (Acceptance Rate 31.2%).

Fengdi Che, Xiru Zhu, Doina Precup, David Meger, Gregory Dudek. Bayesian Q-learning with Imperfect Expert Demonstrations. 3rd Offline RL Workshop. 2022.

Fengdi Che, Xiru Zhu, Tianzi Yang, Tzu-Yang Yu. 3SGAN: 3D Shape Embedded Generative Adversarial Networks. IEEE International Conference on Computer Vision Workshop, 2019.

Xiru Zhu, Fengdi Che, Tianzi Yang, Tzuyang Yu, David Meger, Gregory Dudek. Detecting GAN Generated Errors. ArXiv preprint, 2019.