| 2026 | ACL | Graph Explorer: Training Faithful KG Agents with Visibility-Grounded Supervision. | Yifeng Chen, Sicheng Wan, Tianyi Zhang, Xuezhou Zhang |
| 2025 | AAAI | Efficient Reinforcement Learning in Probabilistic Reward Machines. | Xiaofeng Lin, Xuezhou Zhang |
| 2024 | AAAI | Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption. | Yiding Chen, Xuezhou Zhang, Qiaomin Xie, Xiaojin Zhu |
| 2024 | COLT | Scale-free Adversarial Reinforcement Learning. | Mingyu Chen, Xuezhou Zhang |
| 2023 | AISTATS | Byzantine-Robust Online and Offline Distributed Reinforcement Learning. | Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu |
| 2023 | COLT | Provable Benefits of Representational Transfer in Reinforcement Learning. | Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang |
| 2023 | ICLR | Representation Learning for Low-rank General-sum Markov Games. | Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang |
| 2023 | ICML | Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. | Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang |
| 2022 | AISTATS | Corruption-robust Offline Reinforcement Learning. | Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun |
| 2022 | ICLR | Representation Learning for Online and Offline RL in Low-rank MDPs. | Masatoshi Uehara, Xuezhou Zhang, Wen Sun |
| 2022 | ICML | Optimal Estimation of Policy Gradient via Double Fitted Iteration. | Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang |
| 2022 | ICML | Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. | Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun |
| 2022 | ICML | Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. | Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang |
| 2021 | AAAI | The Sample Complexity of Teaching by Reinforcement on Q-Learning. | Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu |
| 2021 | CogSci | Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. | Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Jerry Zhu |
| 2021 | ICML | Robust Policy Gradient against Strong Data Corruption. | Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun |
| 2021 | WWW | Controllable and Diverse Text Generation in E-commerce. | Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, Aston Zhang, Heng Ji, Tarek F. Abdelzaher |
| 2020 | ICML | Adaptive Reward-Poisoning Attacks against Reinforcement Learning. | Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu |
| 2019 | AISTATS | An Optimal Control Approach to Sequential Machine Teaching. | Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu |
| 2019 | KDD | Axiomatic Interpretability for Multiclass Additive Models. | Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana |
| 2018 | AAAI | Training Set Debugging Using Trusted Items. | Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright |
| 2018 | AISTATS | Teacher Improves Learning by Selecting a Training Subset. | Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu |