About Me

I am a third-year PhD student at Computer Science Department, Stanford University. I am extremely fortunate to be advised Prof. Tengyu Ma. Before that, I studied as an undergraduate at Institute for Interdisciplinary Information Sciences, Tsinghua University (a.k.a. Yao class). I am broadly interested in reinforcement learning theory, online learning and algorithm design.


First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains
Kefan Dong, Tengyu Ma
Workshop on Distribution Shifts: Connecting Methods and Applications, NeurIPS 2022

Asymptotic Instance-Optimal Algorithms for Interactive Decision Making
Kefan Dong, Tengyu Ma

Design of Experiments for Stochastic Contextual Linear Bandits
Andrea Zanette*, Kefan Dong*, Jonathan Lee*, Emma Brunskill
NeurIPS 2021

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature (video, slides)
Kefan Dong, Jiaqi Yang, Tengyu Ma
NeurIPS 2021

Refined Analysis of FPL for Adversarial Markov Decision Processes
Yuanhao Wang, Kefan Dong
Theoretical Foundations of Reinforcement Learning Workshop, ICML 2020

Multinomial Logit Bandit with Low Switching Cost
Kefan Dong*, Yingkai Li*, Qin Zhang, Yuan Zhou
ICML 2020

On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong*, Yuping Luo*, Tengyu Ma
ICML 2020

Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Kefan Dong*, Jian Peng*, Yining Wang*, Yuan Zhou*
COLT 2020

Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
Kefan Dong*, Yuanhao Wang*, Xiaoyu Chen, Liwei Wang
ICLR 2020

Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
NeurIPS 2019