Training Set Debugging Using Trusted Items. - Xuezhou Zhang; Xiaojin Zhu 001; Stephen J. Wright 001 (AAAI, 2018)

Teacher Improves Learning by Selecting a Training Subset. - Yuzhe Ma; Robert Nowak 001; Philippe Rigollet; Xuezhou Zhang; Xiaojin Zhu 001 (AISTATS, 2018)

Training Set Camouflage. - Ayon Sen; Scott Alfeld; Xuezhou Zhang; Ara Vartanian; Yuzhe Ma; Xiaojin Zhu 001 (GameSec, 2018)

Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. - Xuezhou Zhang; Sarah Tan; Paul Koch; Yin Lou; Urszula Chajewska; Rich Caruana (CoRR, 2018)

An Optimal Control Approach to Sequential Machine Teaching. - Laurent Lessard; Xuezhou Zhang; Xiaojin Zhu 001 (AISTATS, 2019)

Axiomatic Interpretability for Multiclass Additive Models. - Xuezhou Zhang; Sarah Tan; Paul Koch; Yin Lou; Urszula Chajewska; Rich Caruana (KDD, 2019)

Policy Poisoning in Batch Reinforcement Learning and Control. - Yuzhe Ma; Xuezhou Zhang; Wen Sun 002; Jerry Zhu (NeurIPS, 2019)

Online Data Poisoning Attack. - Xuezhou Zhang; Xiaojin Zhu 001 (CoRR, 2019)

Policy Poisoning in Batch Reinforcement Learning and Control. - Yuzhe Ma; Xuezhou Zhang; Wen Sun 002; Xiaojin Zhu 001 (CoRR, 2019)

Adaptive Reward-Poisoning Attacks against Reinforcement Learning. - Xuezhou Zhang; Yuzhe Ma; Adish Singla; Xiaojin Zhu 001 (ICML, 2020)

Online Data Poisoning Attacks. - Xuezhou Zhang; Xiaojin Zhu 001; Laurent Lessard (L4DC, 2020)

Task-agnostic Exploration in Reinforcement Learning. - Xuezhou Zhang; Yuzhe Ma; Adish Singla (NeurIPS, 2020)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Nicholas Frosst; Xuezhou Zhang; Rich Caruana; Geoffrey E. Hinton (CoRR, 2020)

The Teaching Dimension of Q-learning. - Xuezhou Zhang; Shubham Kumar Bharti; Yuzhe Ma; Adish Singla; Xiaojin Zhu 001 (CoRR, 2020)

Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. - Yun-Shiuan Chuang; Xuezhou Zhang; Yuzhe Ma; Mark K. Ho; Joseph L. Austerweil; Xiaojin Zhu 001 (CoRR, 2020)

The Sample Complexity of Teaching by Reinforcement on Q-Learning. - Xuezhou Zhang; Shubham Kumar Bharti; Yuzhe Ma; Adish Singla; Xiaojin Zhu 001 (AAAI, 2021)

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 (CogSci, 2021)

Robust Policy Gradient against Strong Data Corruption. - Xuezhou Zhang; Yiding Chen; Xiaojin Zhu 001; Wen Sun 002 (ICML, 2021)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Levi Melnick; Nicholas Frosst; Xuezhou Zhang; Benjamin J. Lengerich; Rich Caruana; Geoffrey E. Hinton (NeurIPS, 2021)

Controllable and Diverse Text Generation in E-commerce. - Huajie Shao; Jun Wang; Haohong Lin; Xuezhou Zhang; Aston Zhang; Heng Ji 001; Tarek F. Abdelzaher (WWW, 2021)

Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments. - Amin Rakhsha; Xuezhou Zhang; Xiaojin Zhu 001; Adish Singla (CoRR, 2021)

Corruption-Robust Offline Reinforcement Learning. - Xuezhou Zhang; Yiding Chen; Jerry Zhu; Wen Sun 002 (CoRR, 2021)

Corruption-robust Offline Reinforcement Learning. - Xuezhou Zhang; Yiding Chen; Xiaojin Zhu 001; Wen Sun 002 (AISTATS, 2022)

Representation Learning for Online and Offline RL in Low-rank MDPs. - Masatoshi Uehara; Xuezhou Zhang; Wen Sun 002 (ICLR, 2022)

Optimal Estimation of Policy Gradient via Double Fitted Iteration. - Chengzhuo Ni; Ruiqi Zhang; Xiang Ji; Xuezhou Zhang; Mengdi Wang (ICML, 2022)

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. - Xuezhou Zhang; Yuda Song 001; Masatoshi Uehara; Mengdi Wang; Alekh Agarwal; Wen Sun 002 (ICML, 2022)

Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. - Ruiqi Zhang; Xuezhou Zhang; Chengzhuo Ni; Mengdi Wang (ICML, 2022)

Provable Defense against Backdoor Policies in Reinforcement Learning. - Shubham Kumar Bharti; Xuezhou Zhang; Adish Singla; Jerry Zhu (NeurIPS, 2022)

Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. - Shuoguang Yang; Xuezhou Zhang; Mengdi Wang (NeurIPS, 2022)

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. - Hui Yuan 002; Chengzhuo Ni; Huazheng Wang; Xuezhou Zhang; Le Cong; Csaba Szepesvári; Mengdi Wang (NeurIPS, 2022)

Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration. - Chengzhuo Ni; Ruiqi Zhang; Xiang Ji; Xuezhou Zhang; Mengdi Wang (CoRR, 2022)

Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization. - Kaixuan Huang; Yu Wu; Xuezhou Zhang; Shenyinying Tu; Qingyun Wu; Mengdi Wang; Huazheng Wang (CoRR, 2022)

Representation Learning for General-sum Low-rank Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Chi Jin 001; Mengdi Wang (CoRR, 2022)

Provable Defense against Backdoor Policies in Reinforcement Learning. - Shubham Kumar Bharti; Xuezhou Zhang; Adish Singla; Xiaojin Zhu 001 (CoRR, 2022)

Byzantine-Robust Online and Offline Distributed Reinforcement Learning. - Yiding Chen; Xuezhou Zhang; Kaiqing Zhang; Mengdi Wang; Xiaojin Zhu 001 (AISTATS, 2023)

Provable Benefits of Representational Transfer in Reinforcement Learning. - Alekh Agarwal; Yuda Song 001; Wen Sun 002; Kaiwen Wang; Mengdi Wang; Xuezhou Zhang (COLT, 2023)

Representation Learning for Low-rank General-sum Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Zihan Ding; Chi Jin 001; Mengdi Wang (ICLR, 2023)

Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. - Jiacheng Guo; Zihao Li; Huazheng Wang; Mengdi Wang; Zhuoran Yang; Xuezhou Zhang (ICML, 2023)

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback. - Canzhe Zhao; Ruofeng Yang; Baoxiang Wang 001; Xuezhou Zhang; Shuai Li 010 (NeurIPS, 2023)

Improved Algorithms for Adversarial Bandits with Unbounded Losses. - Mingyu Chen 012; Xuezhou Zhang (CoRR, 2023)

Federated Multi-Level Optimization over Decentralized Networks. - Shuoguang Yang; Xuezhou Zhang; Mengdi Wang (CoRR, 2023)

Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption. - Yiding Chen; Xuezhou Zhang; Qiaomin Xie; Xiaojin Zhu 001 (AAAI, 2024)

Scale-free Adversarial Reinforcement Learning. - Mingyu Chen 012; Xuezhou Zhang (COLT, 2024)

State-free Reinforcement Learning. - Mingyu Chen 012; Aldo Pacchiano; Xuezhou Zhang (NeurIPS, 2024)

Efficient Reinforcement Learning in Probabilistic Reward Machines. - Xiaofeng Lin; Xuezhou Zhang (AAAI, 2025)

Avoiding exp(Rmax) scaling in RLHF through Preference-based Exploration. - Mingyu Chen 012; Yiding Chen; Wen Sun; Xuezhou Zhang (CoRR, 2025)

Accelerating RL for LLM Reasoning with Optimal Advantage Regression. - Kianté Brantley; Mingyu Chen; Zhaolin Gao; Jason D. Lee; Wen Sun 002; Wenhao Zhan; Xuezhou Zhang (CoRR, 2025)