Tengyang Xie

Hi! I'm a Ph.D. candidate in Computer Science at University of Illinois at Urbana-Champaign (UIUC), where I am fortunate to work with Nan Jiang. My research mainly focuses on interactive machine learning, such as reinforcement learning, contextual bandits, and causal inference. Currently, I am working on two "opposite" directions: batch RL (a.k.a. off-policy/offline RL) -- how to learn from the exploratory data solely, and exploration in RL -- how to explore the environment efficiently. I am also interested in unsupervised/self-supervised learning and the theory of deep neural networks.

Prior to UIUC, I received master degree in Computer Science from University of Massachusetts Amherst. Before that, I obtained bachelor's degree in Physics from University of Science and Technology of China (USTC). I have also spent time at Simons Institute, Amazon AI, Microsoft Research, and Google Research.

Publications

Interaction-Grounded Learning with Action-Inclusive Feedback. [PDF, arXiv]
Tengyang Xie*, Akanksha Saran*, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Conference on Neural Information Processing Systems (NeurIPS) 2022
Complex Feedback in Online Learning Workshop at ICML 2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning. [PDF, arXiv, code, MSR blog]
Ching-An Cheng*, Tengyang Xie*, Nan Jiang, Alekh Agarwal
International Conference on Machine Learning (ICML) 2022 (Outstanding Paper Runner-up Award)

Bellman-consistent Pessimism for Offline Reinforcement Learning. [PDF, arXiv, slides]
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
Conference on Neural Information Processing Systems (NeurIPS) 2021 (Oral, <1%)

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. [PDF, arXiv]
Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
Conference on Neural Information Processing Systems (NeurIPS) 2021

Interaction-Grounded Learning. [PDF, arXiv, add'l supplement]
Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad
International Conference on Machine Learning (ICML) 2021

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. [PDF, arXiv]
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie
Submitted, 2021.

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. [PDF, arXiv]
Philip Amortila*, Nan Jiang*, Tengyang Xie*

Batch Value-function Approximation with Only Realizability. [PDF, arXiv]
Tengyang Xie, Nan Jiang
International Conference on Machine Learning (ICML) 2021

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. [PDF, arXiv]
Tengyang Xie, Nan Jiang
Conference on Uncertainty in Artificial Intelligence (UAI) 2020

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. [PDF, Poster, arXiv]
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Conference on Neural Information Processing Systems (NeurIPS) 2019
Spotlight presentation at the NeurIPS 2018 Workshop on Causal Learning.

Provably Efficient Q-Learning with Low Switching Cost. [PDF, Poster, arXiv]
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Conference on Neural Information Processing Systems (NeurIPS) 2019

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. [PDF, Poster, Link]
Tengyang Xie*, Bo Liu*, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
Conference on Neural Information Processing Systems (NeurIPS) 2018

Privacy Preserving Off-policy Evaluation. [PDF, arXiv]
Tengyang Xie, Philip S. Thomas, Gerome Miklau

(* indicates equal contribution or alphabetic ordering.)

Service

Conference Reviewer/Program Committee: NeurIPS, ICML, AISTATS, AAAI.
Journal Reviewer: Machine Learning Journal.
Workshop Program Committee: Optimization Foundations of RL @ NeurIPS 2019, Theoretical Foundations of RL @ ICML 2020 & 2021, Offline RL @ NeurIPS 2020, RL for Real Life @ ICML 2021.