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2020 – today
- 2024
[c29]Yiding Chen, Xuezhou Zhang, Qiaomin Xie, Xiaojin Zhu:
Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption. AAAI 2024: 11416-11424- [c28]Mingyu Chen, Xuezhou Zhang:
Scale-free Adversarial Reinforcement Learning. COLT 2024: 1068-1101 - [i34]Mingyu Chen, Xuezhou Zhang:
Scale-free Adversarial Reinforcement Learning. CoRR abs/2403.00930 (2024) - [i33]Xiaofeng Lin, Xuezhou Zhang:
Efficient Reinforcement Learning in Probabilistic Reward Machines. CoRR abs/2408.10381 (2024) - [i32]Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang:
State-free Reinforcement Learning. CoRR abs/2409.18439 (2024) - 2023
- [c27]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. AISTATS 2023: 3230-3269 - [c26]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. COLT 2023: 2114-2187 - [c25]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang:
Representation Learning for Low-rank General-sum Markov Games. ICLR 2023 - [c24]Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. ICML 2023: 11967-11997 - [c23]Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li:
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback. NeurIPS 2023 - [i31]Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. CoRR abs/2306.12356 (2023) - [i30]Mingyu Chen, Xuezhou Zhang:
Improved Algorithms for Adversarial Bandits with Unbounded Losses. CoRR abs/2310.01756 (2023) - [i29]Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
:
Federated Multi-Level Optimization over Decentralized Networks. CoRR abs/2310.06217 (2023) - [i28]Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li:
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback. CoRR abs/2311.07876 (2023) - 2022
- [c22]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Corruption-robust Offline Reinforcement Learning. AISTATS 2022: 5757-5773 - [c21]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. ICLR 2022 - [c20]Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang:
Optimal Estimation of Policy Gradient via Double Fitted Iteration. ICML 2022: 16724-16783 - [c19]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML 2022: 26517-26547 - [c18]Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang:
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. ICML 2022: 26713-26749 - [c17]Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Jerry Zhu:
Provable Defense against Backdoor Policies in Reinforcement Learning. NeurIPS 2022 - [c16]Shuoguang Yang, Xuezhou Zhang, Mengdi Wang:
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. NeurIPS 2022 - [c15]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. NeurIPS 2022 - [i27]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR abs/2202.00063 (2022) - [i26]Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang:
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration. CoRR abs/2202.00076 (2022) - [i25]Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang:
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. CoRR abs/2202.04970 (2022) - [i24]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR abs/2205.14571 (2022) - [i23]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. CoRR abs/2206.00165 (2022) - [i22]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. CoRR abs/2206.02092 (2022) - [i21]Shuoguang Yang, Xuezhou Zhang, Mengdi Wang:
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. CoRR abs/2206.10870 (2022) - [i20]Kaixuan Huang, Yu Wu, Xuezhou Zhang, Shenyinying Tu, Qingyun Wu, Mengdi Wang, Huazheng Wang:
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization. CoRR abs/2206.14846 (2022) - [i19]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang:
Representation Learning for General-sum Low-rank Markov Games. CoRR abs/2210.16976 (2022) - [i18]Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Xiaojin Zhu:
Provable Defense against Backdoor Policies in Reinforcement Learning. CoRR abs/2211.10530 (2022) - 2021
- [c14]Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
The Sample Complexity of Teaching by Reinforcement on Q-Learning. AAAI 2021: 10939-10947 - [c13]Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Jerry Zhu:
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. CogSci 2021 - [c12]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. ICML 2021: 12391-12401 - [c11]Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. NeurIPS 2021: 4699-4711 - [c10]Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, Aston Zhang, Heng Ji, Tarek F. Abdelzaher:
Controllable and Diverse Text Generation in E-commerce. WWW 2021: 2392-2401 - [i17]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. CoRR abs/2102.05800 (2021) - [i16]Amin Rakhsha, Xuezhou Zhang, Xiaojin Zhu, Adish Singla:
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments. CoRR abs/2102.08492 (2021) - [i15]Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, Aston Zhang, Heng Ji, Tarek F. Abdelzaher:
Controllable and Diverse Text Generation in E-commerce. CoRR abs/2102.11497 (2021) - [i14]Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun:
Corruption-Robust Offline Reinforcement Learning. CoRR abs/2106.06630 (2021) - [i13]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. CoRR abs/2110.04652 (2021) - 2020
- [c9]Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. ICML 2020: 11225-11234 - [c8]Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard:
Online Data Poisoning Attacks. L4DC 2020: 201-210 - [c7]Xuezhou Zhang, Yuzhe Ma, Adish Singla:
Task-agnostic Exploration in Reinforcement Learning. NeurIPS 2020 - [i12]Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. CoRR abs/2003.12613 (2020) - [i11]Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton:
Neural Additive Models: Interpretable Machine Learning with Neural Nets. CoRR abs/2004.13912 (2020) - [i10]Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
The Teaching Dimension of Q-learning. CoRR abs/2006.09324 (2020) - [i9]Xuezhou Zhang, Yuzhe Ma, Adish Singla:
Task-agnostic Exploration in Reinforcement Learning. CoRR abs/2006.09497 (2020) - [i8]Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Xiaojin Zhu:
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. CoRR abs/2009.02476 (2020)
2010 – 2019
- 2019
- [c6]Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu:
An Optimal Control Approach to Sequential Machine Teaching. AISTATS 2019: 2495-2503 - [c5]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Axiomatic Interpretability for Multiclass Additive Models. KDD 2019: 226-234 - [c4]Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. NeurIPS 2019: 14543-14553 - [i7]Xuezhou Zhang, Xiaojin Zhu:
Online Data Poisoning Attack. CoRR abs/1903.01666 (2019) - [i6]Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. CoRR abs/1910.05821 (2019) - 2018
- [c3]Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright:
Training Set Debugging Using Trusted Items. AAAI 2018: 4482-4489 - [c2]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. AISTATS 2018: 1366-1375 - [c1]Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu:
Training Set Camouflage. GameSec 2018: 59-79 - [i5]Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright:
Training Set Debugging Using Trusted Items. CoRR abs/1801.08019 (2018) - [i4]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. CoRR abs/1802.08946 (2018) - [i3]Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu:
An Optimal Control Approach to Sequential Machine Teaching. CoRR abs/1810.06175 (2018) - [i2]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR abs/1810.09092 (2018) - [i1]Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu:
Training Set Camouflage. CoRR abs/1812.05725 (2018)
Coauthor Index
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last updated on 2024-10-23 20:35 CEST by the dblp team
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