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ORCIDXiaojin Zhu 0001
Xiaojin (Jerry) Zhu
Person information
- affiliation: University of Wisconsin-Madison, Department of Computer Sciences, WI, USA
Other persons with the same name
- Xiaojin Zhu
- Xiaojin Zhu 0002
![[0000-0001-6742-8085]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-6742-8085")
— Shanghai University, School of Mechatronics Engineering and Automation, China - Xiaojin Zhu 0003 — Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China
- Xiaojin Zhu 0004 — Xidian University, Department of Computer Science, Xi'an, China
- Xiaojin Zhu 0005 — Zhejiang Zheneng Taizhou No. 2 Power Generation Company Ltd., China
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2020 – today
- 2024
[c116]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- [c115]Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie:
Optimal Attack and Defense for Reinforcement Learning. AAAI 2024: 14332-14340 - [c114]Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie:
Data Poisoning to Fake a Nash Equilibria for Markov Games. AAAI 2024: 15979-15987 - [c113]Jeremy McMahan, Xiaojin Zhu:
Anytime-Constrained Reinforcement Learning. AISTATS 2024: 4321-4329 - [i56]Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu, Qiaomin Xie:
Inception: Efficiently Computable Misinformation Attacks on Markov Games. CoRR abs/2406.17114 (2024) - [i55]Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy:
Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents. CoRR abs/2407.15881 (2024) - [i54]Shubham Kumar Bharti, Shiyun Cheng, Jihyun Rho, Martina Rao, Xiaojin Zhu:
CHARTOM: A Visual Theory-of-Mind Benchmark for Multimodal Large Language Models. CoRR abs/2408.14419 (2024) - 2023
- [c112]Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie:
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. AAAI 2023: 10426-10434 - [c111]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. AISTATS 2023: 3230-3269 - [i53]Leitian Tao, Xuefeng Du, Xiaojin Zhu, Yixuan Li:
Non-Parametric Outlier Synthesis. CoRR abs/2303.02966 (2023) - [i52]Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy:
Mechanism Design for Collaborative Normal Mean Estimation. CoRR abs/2306.06351 (2023) - [i51]Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie:
On Faking a Nash Equilibrium. CoRR abs/2306.08041 (2023) - [i50]Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu, Qiaomin Xie:
VISER: A Tractable Solution Concept for Games with Information Asymmetry. CoRR abs/2307.09652 (2023) - [i49]Xuefeng Du, Yiyou Sun, Xiaojin Zhu, Yixuan Li:
Dream the Impossible: Outlier Imagination with Diffusion Models. CoRR abs/2309.13415 (2023) - [i48]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 abs/2311.00582 (2023) - [i47]Jeremy McMahan, Xiaojin Zhu:
Anytime-Constrained Reinforcement Learning. CoRR abs/2311.05511 (2023) - [i46]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 abs/2311.10127 (2023) - [i45]Shubham Kumar Bharti, Stephen Wright, Adish Singla, Xiaojin Zhu:
Optimally Teaching a Linear Behavior Cloning Agent. CoRR abs/2311.15399 (2023) - [i44]Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie:
Optimal Attack and Defense for Reinforcement Learning. CoRR abs/2312.00198 (2023) - 2022
- [c110]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Corruption-robust Offline Reinforcement Learning. AISTATS 2022: 5757-5773 - [c109]Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li:
Out-of-Distribution Detection with Deep Nearest Neighbors. ICML 2022: 20827-20840 - [c108]Yuzhe Ma, Young Wu, Xiaojin Zhu:
Game Redesign in No-regret Game Playing. IJCAI 2022: 3321-3327 - [i43]Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li:
Out-of-distribution Detection with Deep Nearest Neighbors. CoRR abs/2204.06507 (2022) - [i42]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. CoRR abs/2206.00165 (2022) - [i41]Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie:
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. CoRR abs/2206.01888 (2022) - [i40]Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Xiaojin Zhu:
Provable Defense against Backdoor Policies in Reinforcement Learning. CoRR abs/2211.10530 (2022) - 2021
- [j11]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): 770-786 (2021) - [j10]Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla:
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. J. Mach. Learn. Res. 22: 210:1-210:45 (2021) - [j9]Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh:
Provable training set debugging for linear regression. Mach. Learn. 110(10): 2763-2834 (2021) - [c107]Yuzhe Ma, Jon A. Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu:
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems. AAAI 2021: 8865-8873 - [c106]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 - [c105]Jinmeng Rao, Song Gao, Xiaojin Zhu:
VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and Visualization Platform Using Deep Reinforcement Learning. GeoAI@SIGSPATIAL 2021: 43-46 - [c104]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. ICML 2021: 12391-12401 - [i39]Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun:
Robust Policy Gradient against Strong Data Corruption. CoRR abs/2102.05800 (2021) - [i38]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) - [i37]Yuzhe Ma, Young Wu, Xiaojin Zhu:
Game Redesign in No-regret Game Playing. CoRR abs/2110.11763 (2021) - 2020
- [c103]Yiding Chen, Xiaojin Zhu:
Optimal Attack against Autoregressive Models by Manipulating the Environment. AAAI 2020: 3545-3552 - [c102]Ayon Sen, Xiaojin Zhu, Erin Marshall, Robert D. Nowak:
Popular Imperceptibility Measures in Visual Adversarial Attacks are Far from Human Perception. GameSec 2020: 188-199 - [c101]Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla:
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. ICML 2020: 7974-7984 - [c100]Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. ICML 2020: 11225-11234 - [c99]Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard:
Online Data Poisoning Attacks. L4DC 2020: 201-210 - [i36]Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. CoRR abs/2003.12613 (2020) - [i35]Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla:
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning. CoRR abs/2003.12909 (2020) - [i34]Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh:
Theory of Machine Learning Debugging via M-estimation. CoRR abs/2006.09009 (2020) - [i33]Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
The Teaching Dimension of Q-learning. CoRR abs/2006.09324 (2020) - [i32]Ayon Sen, Christopher R. Cox, Matthew Cooper Borkenhagen, Mark S. Seidenberg, Xiaojin Zhu:
Learning to Read through Machine Teaching. CoRR abs/2006.16470 (2020) - [i31]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) - [i30]Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, Adish Singla:
Preference-Based Batch and Sequential Teaching. CoRR abs/2010.10012 (2020) - [i29]Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla:
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. CoRR abs/2011.10824 (2020) - [i28]Yuzhe Ma, Jon A. Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu:
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems. CoRR abs/2012.08704 (2020)
2010 – 2019
- 2019
- [c98]Martina A. Rau, Ayon Sen, Xiaojin Zhu:
Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings. AIED (1) 2019: 406-418 - [c97]Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu:
An Optimal Control Approach to Sequential Machine Teaching. AISTATS 2019: 2495-2503 - [c96]Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu:
Teaching a black-box learner. ICML 2019: 1547-1555 - [c95]Yuzhe Ma, Xiaojin Zhu, Justin Hsu:
Data Poisoning against Differentially-Private Learners: Attacks and Defenses. IJCAI 2019: 4732-4738 - [c94]Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin (Jerry) Zhu, Adish Singla:
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models. NeurIPS 2019: 9195-9205 - [i27]Yiding Chen, Xiaojin Zhu:
Optimal Adversarial Attack on Autoregressive Models. CoRR abs/1902.00202 (2019) - [i26]Xuezhou Zhang, Xiaojin Zhu:
Online Data Poisoning Attack. CoRR abs/1903.01666 (2019) - [i25]Yuzhe Ma, Xiaojin Zhu, Justin Hsu:
Data Poisoning against Differentially-Private Learners: Attacks and Defenses. CoRR abs/1903.09860 (2019) - [i24]Owen Levin, Zihang Meng, Vikas Singh, Xiaojin Zhu:
Fooling Computer Vision into Inferring the Wrong Body Mass Index. CoRR abs/1905.06916 (2019) - [i23]Ayon Sen, Xiaojin Zhu, Liam Marshall, Robert D. Nowak:
Should Adversarial Attacks Use Pixel p-Norm? CoRR abs/1906.02439 (2019) - [i22]Vicki M. Bier, Paul B. Kantor, Gary Lupyan, Xiaojin Zhu:
Can We Distinguish Machine Learning from Human Learning? CoRR abs/1910.03466 (2019) - [i21]Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu:
Policy Poisoning in Batch Reinforcement Learning and Control. CoRR abs/1910.05821 (2019) - [i20]Zhiyan Ding, Yiding Chen, Qin Li, Xiaojin Zhu:
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent. CoRR abs/1910.08212 (2019) - [i19]Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, Adish Singla:
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models. CoRR abs/1910.10944 (2019) - [i18]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 abs/1910.14147 (2019) - 2018
- [c93]Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright:
Training Set Debugging Using Trusted Items. AAAI 2018: 4482-4489 - [c92]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. AISTATS 2018: 1366-1375 - [c91]Felice Resnik, Amy Bellmore, Xiaojin (Jerry) Zhu, Wei Zhang:
Using Machine Learning to Understand Changes in How Youth Discuss Bullying With Celebrities on Social Media. APAScience 2018: 34:1 - [c90]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: 194-199 - [c89]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 - [c88]Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu:
Training Set Camouflage. GameSec 2018: 59-79 - [c87]Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu:
Data Poisoning Attacks in Contextual Bandits. GameSec 2018: 186-204 - [c86]Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu:
Adversarial Attacks on Stochastic Bandits. NeurIPS 2018: 3644-3653 - [e2]Chuck Kalish, Martina A. Rau, Xiaojin (Jerry) Zhu, Timothy T. Rogers:
Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018, Madison, WI, USA, July 25-28, 2018. cognitivesciencesociety.org 2018, ISBN 978-0-9911967-8-4 [contents] - [i17]Xiaojin Zhu, Adish Singla, Sandra Zilles, Anna N. Rafferty:
An Overview of Machine Teaching. CoRR abs/1801.05927 (2018) - [i16]Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright:
Training Set Debugging Using Trusted Items. CoRR abs/1801.08019 (2018) - [i15]Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. CoRR abs/1802.08946 (2018) - [i14]Evan Hernandez, Ara Vartanian, Xiaojin Zhu:
Program Synthesis from Visual Specification. CoRR abs/1806.00938 (2018) - [i13]Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu:
Data Poisoning Attacks in Contextual Bandits. CoRR abs/1808.05760 (2018) - [i12]Wei Zhang, Fan Bu, Derek Owens-Oas, Katherine A. Heller, Xiaojin Zhu:
Learning Root Source with Marked Multivariate Hawkes Processes. CoRR abs/1809.03648 (2018) - [i11]Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu:
An Optimal Control Approach to Sequential Machine Teaching. CoRR abs/1810.06175 (2018) - [i10]Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu:
Adversarial Attacks on Stochastic Bandits. CoRR abs/1810.12188 (2018) - [i9]Xiaojin Zhu:
An Optimal Control View of Adversarial Machine Learning. CoRR abs/1811.04422 (2018) - [i8]Ayon Sen, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, Xiaojin Zhu:
Training Set Camouflage. CoRR abs/1812.05725 (2018) - 2017
- [j8]Vraj Shah, Arun Kumar, Xiaojin Zhu:
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? Proc. VLDB Endow. 11(3): 366-379 (2017) - [c85]Scott Alfeld, Xiaojin Zhu, Paul Barford:
Explicit Defense Actions Against Test-Set Attacks. AAAI 2017: 1274-1280 - [c84]Shalini Ghosh, Patrick Lincoln, Ashish Tiwari, Xiaojin Zhu:
Trusted Machine Learning: Model Repair and Data Repair for Probabilistic Models. AAAI Workshops 2017 - [c83]Xiaojin Zhu, Ji Liu, Manuel Lopes:
No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously. IJCAI 2017: 3588-3594 - [c82]Paul N. Bennett, David Maxwell Chickering, Christopher Meek, Xiaojin Zhu:
Algorithms for Active Classifier Selection: Maximizing Recall with Precision Constraints. WSDM 2017: 711-719 - [e1]Aarti Singh, Xiaojin (Jerry) Zhu:
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA. Proceedings of Machine Learning Research 54, PMLR 2017 [contents] - [r2]Xiaojin Zhu:
Semi-supervised Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1142-1147 - [i7]Vraj Shah, Arun Kumar, Xiaojin Zhu:
Stop That Join! Discarding Dimension Tables when Learning High Capacity Classifiers. CoRR abs/1704.00485 (2017) - [i6]Sanjit A. Seshia, Xiaojin (Jerry) Zhu, Andreas Krause, Susmit Jha:
Machine Learning and Formal Method (Dagstuhl Seminar 17351). Dagstuhl Reports 7(8): 55-73 (2017) - 2016
- [j7]Ji Liu, Xiaojin Zhu:
The Teaching Dimension of Linear Learners. J. Mach. Learn. Res. 17: 162:1-162:25 (2016) - [c81]Scott Alfeld, Xiaojin Zhu, Paul Barford:
Data Poisoning Attacks against Autoregressive Models. AAAI 2016: 1452-1458 - [c80]Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu:
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS 2016: 139-148 - [c79]Ji Liu, Xiaojin Zhu, Hrag Ohannessian:
The Teaching Dimension of Linear Learners. ICML 2016: 117-126 - [c78]Jina Suh, Xiaojin Zhu, Saleema Amershi:
The Label Complexity of Mixed-Initiative Classifier Training. ICML 2016: 2800-2809 - [c77]Xiaojin Zhu, Ara Vartanian, Manish Bansal, Duy Nguyen, Luke Brandl:
Stochastic Multiresolution Persistent Homology Kernel. IJCAI 2016: 2449-2457 - [c76]Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu:
Active Learning with Oracle Epiphany. NIPS 2016: 2820-2828 - [c75]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: 19-34 - [c74]Scott Alfeld, Xiaojin Zhu, Paul Barford:
Machine Teaching as Search. SOCS 2016: 117-118 - [i5]Christopher Meek, Patrice Y. Simard, Xiaojin Zhu:
Analysis of a Design Pattern for Teaching with Features and Labels. CoRR abs/1611.05950 (2016) - 2015
- [j6]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: 305-314 (2015) - [c73]Shike Mei, Xiaojin Zhu:
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners. AAAI 2015: 2871-2877 - [c72]Xiaojin Zhu:
Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education. AAAI 2015: 4083-4087 - [c71]Shike Mei, Xiaojin Zhu:
The Security of Latent Dirichlet Allocation. AISTATS 2015 - [c70]Bryan R. Gibson, Timothy T. Rogers, Chuck Kalish, Xiaojin Zhu:
What causes category-shifting in human semi-supervised learning? CogSci 2015 - [c69]Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu:
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. COLT 2015: 503-522 - [c68]Newsha Ardalani, Clint Lestourgeon, Karthikeyan Sankaralingam, Xiaojin Zhu:
Cross-architecture performance prediction (XAPP) using CPU code to predict GPU performance. MICRO 2015: 725-737 - [c67]Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan:
Human Memory Search as Initial-Visit Emitting Random Walk. NIPS 2015: 1072-1080 - [i4]Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu:
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. CoRR abs/1506.08760 (2015) - [i3]Ji Liu, Xiaojin Zhu:
The Teaching Dimension of Linear Learners. CoRR abs/1512.02181 (2015) - 2014
- [j5]Mark W. Liu, Mutlu Ozdogan, Xiaojin Zhu:
Crop Type Classification by Simultaneous Use of Satellite Images of Different Resolutions. IEEE Trans. Geosci. Remote. Sens. 52(6): 3637-3649 (2014) - [c66]Shike Mei, Han Li, Jing Fan, Xiaojin Zhu, Charles R. Dyer:
Inferring air pollution by sniffing social media. ASONAM 2014: 534-539 - [c65]Jun-Ming Xu, Hsun-Chih Huang, Amy Bellmore, Xiaojin Zhu:
School Bullying in Twitter and Weibo: A Comparative Study. ICWSM 2014 - [c64]Kaustubh R. Patil, Xiaojin Zhu, Lukasz Kopec, Bradley C. Love:
Optimal Teaching for Limited-Capacity Human Learners. NIPS 2014: 2465-2473 - [c63]Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, Xiaojin Zhu:
Corleone: hands-off crowdsourcing for entity matching. SIGMOD Conference 2014: 601-612 - 2013
- [j4]Bryan R. Gibson, Timothy T. Rogers, Xiaojin Zhu:
Human Semi-Supervised Learning. Top. Cogn. Sci. 5(1): 132-172 (2013) - [c62]Kwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers:
Learning from Human-Generated Lists. ICML (3) 2013: 181-189 - [c61]Xiaojin Zhu:
Persistent Homology: An Introduction and a New Text Representation for Natural Language Processing. IJCAI 2013: 1953-1959 - [c60]Jun-Ming Xu, Aniruddha Bhargava, Robert D. Nowak, Xiaojin Zhu:
Socioscope: Spatio-Temporal Signal Recovery from Social Media (Extended Abstract). IJCAI 2013: 3096-3100 - [c59]