Xiaojin Zhu

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 94

Top Venues

CoRR

AAAI

AISTATS

ICML

Publications

Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie
Data Poisoning to Fake a Nash Equilibria for Markov Games. AAAI (2024)
Yiding Chen, Xuezhou Zhang, Qiaomin Xie, Xiaojin Zhu
Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption. AAAI (2024)
Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu, Qiaomin Xie
Inception: Efficiently Computable Misinformation Attacks on Markov Games. CoRR (2024)
Jeremy McMahan, Xiaojin Zhu
Anytime-Constrained Reinforcement Learning. AISTATS (2024)
Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie
Optimal Attack and Defense for Reinforcement Learning. AAAI (2024)
Leitian Tao, Xuefeng Du, Xiaojin Zhu, Yixuan Li
Non-Parametric Outlier Synthesis. CoRR (2023)
Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu, Qiaomin Xie
VISER: A Tractable Solution Concept for Games with Information Asymmetry. CoRR (2023)
Young Wu, Jeremy McMahan, Yiding Chen, Yudong Chen, Xiaojin Zhu, Qiaomin Xie
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value. CoRR (2023)
Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. AAAI (2023)
Jeremy McMahan, Xiaojin Zhu
Anytime-Constrained Reinforcement Learning. CoRR (2023)
Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. AISTATS (2023)
Xuefeng Du, Yiyou Sun, Xiaojin Zhu, Yixuan Li
Dream the Impossible: Outlier Imagination with Diffusion Models. CoRR (2023)
Shubham Kumar Bharti, Stephen Wright, Adish Singla, Xiaojin Zhu
Optimally Teaching a Linear Behavior Cloning Agent. CoRR (2023)
Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie
On Faking a Nash Equilibrium. CoRR (2023)
Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
Mechanism Design for Collaborative Normal Mean Estimation. CoRR (2023)
Ara Vartanian, Xiaoxi Sun, Yun-Shiuan Chuang, Siddharth Suresh, Xiaojin Zhu, Timothy T. Rogers
Learning interactions to boost human creativity with bandits and GPT-4. CoRR (2023)
Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie
Optimal Attack and Defense for Reinforcement Learning. CoRR (2023)
Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Xiaojin Zhu
Provable Defense against Backdoor Policies in Reinforcement Learning. CoRR (2022)
Yuzhe Ma, Young Wu, Xiaojin Zhu
Game Redesign in No-regret Game Playing. IJCAI (2022)
Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li
Out-of-distribution Detection with Deep Nearest Neighbors. CoRR (2022)
Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. CoRR (2022)
Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li
Out-of-Distribution Detection with Deep Nearest Neighbors. ICML (2022)
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun
Corruption-robust Offline Reinforcement Learning. AISTATS (2022)
Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. CoRR (2022)
Amin Rakhsha, Xuezhou Zhang, Xiaojin Zhu, Adish Singla
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments. CoRR (2021)
Chao Wang, Xiaojin Zhu, Shiyu Zhou, Yingqing Zhou
Bayesian learning of structures of ordered block graphical models with an application on multistage manufacturing processes. IISE Trans. 53 (7) (2021)
Yuzhe Ma, Jon A. Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems. AAAI (2021)
Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
Provable training set debugging for linear regression. Mach. Learn. 110 (10) (2021)
Jinmeng Rao, Song Gao, Xiaojin Zhu
VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and Visualization Platform Using Deep Reinforcement Learning. GeoAI@SIGSPATIAL (2021)
Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu
The Sample Complexity of Teaching by Reinforcement on Q-Learning. AAAI (2021)
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. J. Mach. Learn. Res. 22 (2021)
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun
Robust Policy Gradient against Strong Data Corruption. ICML (2021)
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun
Robust Policy Gradient against Strong Data Corruption. CoRR (2021)
Yuzhe Ma, Young Wu, Xiaojin Zhu
Game Redesign in No-regret Game Playing. CoRR (2021)
Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, Adish Singla
Preference-Based Batch and Sequential Teaching. CoRR (2020)
Ayon Sen, Xiaojin Zhu, Erin Marshall, Robert D. Nowak
Popular Imperceptibility Measures in Visual Adversarial Attacks are Far from Human Perception. GameSec (2020)
Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. ICML (2020)
Ayon Sen, Christopher R. Cox, Matthew Cooper Borkenhagen, Mark S. Seidenberg, Xiaojin Zhu
Learning to Read through Machine Teaching. CoRR (2020)
Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh
Theory of Machine Learning Debugging via M-estimation. CoRR (2020)
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. CoRR (2020)
Yuzhe Ma, Jon A. Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems. CoRR (2020)
Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. CoRR (2020)
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. CoRR (2020)
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. ICML (2020)
Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu
The Teaching Dimension of Q-learning. CoRR (2020)
Yiding Chen, Xiaojin Zhu
Optimal Attack against Autoregressive Models by Manipulating the Environment. AAAI (2020)
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 (2020)
Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard
Online Data Poisoning Attacks. L4DC (2020)
Yiding Chen, Xiaojin Zhu
Optimal Adversarial Attack on Autoregressive Models. CoRR (2019)
Xuezhou Zhang, Xiaojin Zhu
Online Data Poisoning Attack. CoRR (2019)
Owen Levin, Zihang Meng, Vikas Singh, Xiaojin Zhu
Fooling Computer Vision into Inferring the Wrong Body Mass Index. CoRR (2019)
Martina A. Rau, Ayon Sen, Xiaojin Zhu
Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings. AIED (1) (2019)
Ayon Sen, Xiaojin Zhu, Liam Marshall, Robert D. Nowak
Should Adversarial Attacks Use Pixel p-Norm? CoRR (2019)
Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. CoRR (2019)
Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu
Policy Poisoning in Batch Reinforcement Learning and Control. CoRR (2019)
Zhiyan Ding, Yiding Chen, Qin Li, Xiaojin Zhu
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent. CoRR (2019)
Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu
An Optimal Control Approach to Sequential Machine Teaching. AISTATS (2019)
Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Data Poisoning against Differentially-Private Learners: Attacks and Defenses. CoRR (2019)
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu
Teaching a black-box learner. ICML (2019)
Vicki Bier, Paul B. Kantor, Gary Lupyan, Xiaojin Zhu
Can We Distinguish Machine Learning from Human Learning? CoRR (2019)
Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Data Poisoning against Differentially-Private Learners: Attacks and Defenses. IJCAI (2019)
Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, Adish Singla
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models. CoRR (2019)
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu
Adversarial Attacks on Stochastic Bandits. CoRR (2018)
Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
Data Poisoning Attacks in Contextual Bandits. GameSec (2018)
Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright
Training Set Debugging Using Trusted Items. CoRR (2018)
Evan Hernandez, Ara Vartanian, Xiaojin Zhu
Program Synthesis from Visual Specification. CoRR (2018)
Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu
Teacher Improves Learning by Selecting a Training Subset. CoRR (2018)
Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu
Training Set Camouflage. GameSec (2018)
Xiaojin Zhu
An Optimal Control View of Adversarial Machine Learning. CoRR (2018)
Wei Zhang, Fan Bu, Derek Owens-Oas, Katherine Heller, Xiaojin Zhu
Learning Root Source with Marked Multivariate Hawkes Processes. CoRR (2018)
Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu
An Optimal Control Approach to Sequential Machine Teaching. CoRR (2018)
Shalini Ghosh, Susmit Jha, Ashish Tiwari, Patrick Lincoln, Xiaojin Zhu
Model, Data and Reward Repair: Trusted Machine Learning for Markov Decision Processes. DSN Workshops (2018)
Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
Data Poisoning Attacks in Contextual Bandits. CoRR (2018)
Ayon Sen, Purav Patel, Martina A. Rau, Blake Mason, Robert Nowak, Timothy T. Rogers, Xiaojin Zhu
Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice. EDM (2018)
Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu
Training Set Camouflage. CoRR (2018)
Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu
Teacher Improves Learning by Selecting a Training Subset. AISTATS (2018)
Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright
Training Set Debugging Using Trusted Items. AAAI (2018)
Xiaojin Zhu, Adish Singla, Sandra Zilles, Anna N. Rafferty
An Overview of Machine Teaching. CoRR (2018)
Shalini Ghosh, Patrick Lincoln, Ashish Tiwari, Xiaojin Zhu
Trusted Machine Learning: Model Repair and Data Repair for Probabilistic Models. AAAI Workshops (2017)
Scott Alfeld, Xiaojin Zhu, Paul Barford
Explicit Defense Actions Against Test-Set Attacks. AAAI (2017)
Vraj Shah, Arun Kumar, Xiaojin Zhu
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? Proc. VLDB Endow. 11 (3) (2017)
Vraj Shah, Arun Kumar, Xiaojin Zhu
Stop That Join! Discarding Dimension Tables when Learning High Capacity Classifiers. CoRR (2017)
Xiaojin Zhu, Ji Liu, Manuel Lopes
No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously. IJCAI (2017)
Paul N. Bennett, David Maxwell Chickering, Christopher Meek, Xiaojin Zhu
Algorithms for Active Classifier Selection: Maximizing Recall with Precision Constraints. WSDM (2017)
Christopher Meek, Patrice Y. Simard, Xiaojin Zhu
Analysis of a Design Pattern for Teaching with Features and Labels. CoRR (2016)
Scott Alfeld, Xiaojin Zhu, Paul Barford
Machine Teaching as Search. SOCS (2016)
Xiaojin Zhu, Ara Vartanian, Manish Bansal, Duy Nguyen, Luke Brandl
Stochastic Multiresolution Persistent Homology Kernel. IJCAI (2016)
Ji Liu, Xiaojin Zhu, Hrag Ohannessian
The Teaching Dimension of Linear Learners. ICML (2016)
Ji Liu, Xiaojin Zhu
The Teaching Dimension of Linear Learners. J. Mach. Learn. Res. 17 (2016)
Jina Suh, Xiaojin Zhu, Saleema Amershi
The Label Complexity of Mixed-Initiative Classifier Training. ICML (2016)
Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS (2016)
Scott Alfeld, Xiaojin Zhu, Paul Barford
Data Poisoning Attacks against Autoregressive Models. AAAI (2016)
Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu
Active Learning with Oracle Epiphany. NIPS (2016)
Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xiaojin Zhu
To Join or Not to Join?: Thinking Twice about Joins before Feature Selection. SIGMOD Conference (2016)
Bryan R. Gibson, Timothy T. Rogers, Chuck Kalish, Xiaojin Zhu
What causes category-shifting in human semi-supervised learning? CogSci (2015)
Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. CoRR (2015)
Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. COLT (2015)
Ji Liu, Xiaojin Zhu
The Teaching Dimension of Linear Learners. CoRR (2015)
Amy Bellmore, Angela J. Calvin, Jun-Ming Xu, Xiaojin Zhu
The five W's of "bullying" on Twitter: Who, What, Why, Where, and When. Comput. Hum. Behav. 44 (2015)
Shike Mei, Xiaojin Zhu
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners. AAAI (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Xiaojin Zhu