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Semantic ScholarXiaojin 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 0002 — 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
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2020 – today
- 2020
[i29]Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu:
Adaptive Reward-Poisoning Attacks against Reinforcement Learning. CoRR abs/2003.12613 (2020)- [i28]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)
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 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 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. topiCS 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]Jun-Ming Xu, Benjamin Burchfiel, Xiaojin Zhu, Amy Bellmore:
An Examination of Regret in Bullying Tweets. HLT-NAACL 2013: 697-702 - [c58]Xiaojin Zhu:
Machine Teaching for Bayesian Learners in the Exponential Family. NIPS 2013: 1905-1913 - [i2]Xiaojin Zhu:
Machine Teaching for Bayesian Learners. CoRR abs/1306.4947 (2013) - 2012
- [j3]Jun-Ming Xu, Xiaojin Zhu, Timothy T. Rogers:
Metric Learning for Estimating Psychological Similarities. ACM Trans. Intell. Syst. Technol. 3(3): 55:1-55:22 (2012) - [c57]Xiaojin Zhu, Xu Zhang, Hui Wang, Chunchan Li, Lili Yan, Qingkai Liu, Zhaohui Ren:
Research on peripheral nerve conduction block by high frequency alternating current stimulation. BMEI 2012: 582-586 - [c56]Qian Dong, Guoqing Chen, Qingkai Liu, Zhaohui Ren, Dewu Yang, Nan Cui, Xiaojin Zhu, Xu Zhang:
Analysis of functional restoration of the lower urinary tract after spinal cord injury using functional electrical stimulation on pudendal nerve. BMEI 2012: 716-720 - [c55]Burr Settles, Xiaojin Zhu:
Behavioral Factors in Interactive Training of Text Classifiers. HLT-NAACL 2012: 563-567 - [c54]Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, Amy Bellmore:
Learning from Bullying Traces in Social Media. HLT-NAACL 2012: 656-666 - [c53]Jun-Ming Xu, Aniruddha Bhargava, Robert D. Nowak, Xiaojin Zhu:
Socioscope: Spatio-temporal Signal Recovery from Social Media. ECML/PKDD (2) 2012: 644-659 - [i1]Jun-Ming Xu, Aniruddha Bhargava, Robert D. Nowak, Xiaojin Zhu:
Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media. CoRR abs/1204.2248 (2012) - 2011
- [c52]Andrew B. Goldberg, Xiaojin Zhu, Alex Furger, Jun-Ming Xu:
OASIS: Online Active Semi-Supervised Learning. AAAI 2011 - [c51]Xiaojin Zhu, Bryan R. Gibson, Timothy T. Rogers:
Co-Training as a Human Collaboration Policy. AAAI 2011 - [c50]Chen Yu, Jun-Ming Xu, Xiaojin Zhu:
Word Learning through Sensorimotor Child-Parent Interaction: A Feature Selection Approach. CogSci 2011 - [c49]Nathan E. Rosenblum, Xiaojin Zhu, Barton P. Miller:
Who Wrote This Code? Identifying the Authors of Program Binaries. ESORICS 2011: 172-189 - [c48]David Andrzejewski, Xiaojin Zhu, Mark Craven, Benjamin Recht:
A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic. IJCAI 2011: 1171-1177 - [c47]Mariyam Mirza, Paul Barford, Xiaojin Zhu, Suman Banerjee, Michael Blodgett:
Fingerprinting 802.11 rate adaption algorithms. INFOCOM 2011: 1161-1169 - [c46]Nathan E. Rosenblum, Barton P. Miller, Xiaojin Zhu:
Recovering the toolchain provenance of binary code. ISSTA 2011: 100-110 - [c45]Shilin Ding, Grace Wahba, Xiaojin (Jerry) Zhu:
Learning Higher-Order Graph Structure with Features by Structure Penalty. NIPS 2011: 253-261 - [c44]Faisal Khan, Xiaojin (Jerry) Zhu, Bilge Mutlu:
How Do Humans Teach: On Curriculum Learning and Teaching Dimension. NIPS 2011: 1449-1457 - 2010
- [j2]Mariyam Mirza, Joel Sommers, Paul Barford, Xiaojin Zhu:
A Machine Learning Approach to TCP Throughput Prediction. IEEE/ACM Trans. Netw. 18(4): 1026-1039 (2010) - [c43]Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish:
Cognitive Models of Test-Item Effects in Human Category Learning. ICML 2010: 1247-1254 - [c42]Bryan R. Gibson, Xiaojin Zhu, Timothy T. Rogers, Chuck Kalish, Joseph Harrison:
Humans Learn Using Manifolds, Reluctantly. NIPS 2010: 730-738 - [c41]Andrew B. Goldberg, Xiaojin Zhu, Ben Recht, Jun-Ming Xu, Robert D. Nowak:
Transduction with Matrix Completion: Three Birds with One Stone. NIPS 2010: 757-765 - [c40]Nathan E. Rosenblum, Barton P. Miller, Xiaojin Zhu:
Extracting compiler provenance from program binaries. PASTE 2010: 21-28 - [r1]Xiaojin Zhu:
Semi-Supervised Learning. Encyclopedia of Machine Learning 2010: 892-897
2000 – 2009
- 2009
- [b1]Xiaojin Zhu, Andrew B. Goldberg:
Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009 - [c39]Xiaojin Zhu, Andrew B. Goldberg, Tushar Khot:
Some new directions in graph-based semi-supervised learning. ICME 2009: 1504-1507 - [c38]David Andrzejewski, Xiaojin Zhu, Mark Craven:
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. ICML 2009: 25-32 - [c37]Andrew B. Goldberg, Nathanael Fillmore, David Andrzejewski, Zhiting Xu, Bryan R. Gibson, Xiaojin Zhu:
May All Your Wishes Come True: A Study of Wishes and How to Recognize Them. HLT-NAACL 2009: 263-271 - [c36]Xiaojin Zhu, Timothy T. Rogers, Bryan R. Gibson:
Human Rademacher Complexity. NIPS 2009: 2322-2330 - [c35]Mariyam Mirza, Kevin Springborn, Suman Banerjee, Paul Barford, Michael Blodgett, Xiaojin Zhu:
On The Accuracy of TCP Throughput Prediction for Opportunistic Wireless Networks. SECON 2009: 1-9 - [c34]Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert D. Nowak:
Multi-Manifold Semi-Supervised Learning. AISTATS 2009: 169-176 - 2008
- [c33]Nathan E. Rosenblum, Xiaojin Zhu, Barton P. Miller, Karen Hunt:
Learning to Analyze Binary Computer Code. AAAI 2008: 798-804 - [c32]Xiaojin Zhu, Michael Coen, Shelley Prudom, Ricki Colman, Joseph W. Kemnitz:
Online Learning in Monkeys. AAAI 2008: 1506-1507 - [c31]John Blitzer, Xiaojin Zhu:
Semi-Supervised Learning for Natural Language Processing. ACL (Tutorial Abstracts) 2008: 3 - [c30]Xiaojin Zhu, Andrew B. Goldberg, Michael Rabbat, Robert D. Nowak:
Learning Bigrams from Unigrams. ACL 2008: 656-664 - [c29]Andrew B. Goldberg, Xiaojin Zhu, Charles R. Dyer, Mohamed Eldawy, Lijie Heng:
Easy as ABC? Facilitating Pictorial Communication via Semantically Enhanced Layout. CoNLL 2008: 119-126 - [c28]Pedro DeRose, Xiaoyong Chai, Byron J. Gao, Warren Shen, AnHai Doan, Philip Bohannon, Xiaojin Zhu:
Building Community Wikipedias: A Machine-Human Partnership Approach. ICDE 2008: 646-655 - [c27]Rui M. Castro, Charles Kalish, Robert D. Nowak, Ruichen Qian, Timothy T. Rogers, Xiaojin Zhu:
Human Active Learning. NIPS 2008: 241-248 - [c26]Aarti Singh, Robert D. Nowak, Xiaojin Zhu:
Unlabeled data: Now it helps, now it doesn't. NIPS 2008: 1513-1520 - [c25]Andrew B. Goldberg, Ming Li, Xiaojin Zhu:
Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML/PKDD (1) 2008: 393-407 - 2007
- [c24]Xiaojin Zhu, Andrew B. Goldberg:
Kernel Regression with Order Preferences. AAAI 2007: 681-687 - [c23]Xiaojin Zhu, Timothy T. Rogers, Ruichen Qian, Chuck Kalish:
Humans Perform Semi-Supervised Classification Too. AAAI 2007: 864-870 - [c22]Xiaojin Zhu, Andrew B. Goldberg, Mohamed Eldawy, Charles R. Dyer, Bradley Strock:
A Text-to-Picture Synthesis System for Augmenting Communication. AAAI 2007: 1590-1596 - [c21]David Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu:
Statistical Debugging Using Latent Topic Models. ECML 2007: 6-17 - [c20]Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu:
A Topic Model for Word Sense Disambiguation. EMNLP-CoNLL 2007: 1024-1033 - [c19]Jurgen Van Gael, Xiaojin Zhu:
Correlation Clustering for Crosslingual Link Detection. IJCAI 2007: 1744-1749 - [c18]Gregory Druck, Chris Pal, Andrew McCallum, Xiaojin Zhu:
Semi-supervised classification with hybrid generative/discriminative methods. KDD 2007: 280-289 - [c17]Xiaojin Zhu, Andrew B. Goldberg, Jurgen Van Gael, David Andrzejewski:
Improving Diversity in Ranking using Absorbing Random Walks. HLT-NAACL 2007: 97-104 - [c16]Mariyam Mirza, Joel Sommers, Paul Barford, Xiaojin Zhu:
A machine learning approach to TCP throughput prediction. SIGMETRICS 2007: 97-108 - [c15]Andrew B. Goldberg, Xiaojin Zhu, Stephen J. Wright:
Dissimilarity in Graph-Based Semi-Supervised Classification. AISTATS 2007: 155-162 - 2006
- [c14]Andrew B. Goldberg, David Andrzejewski, Jurgen Van Gael, Burr Settles, Xiaojin Zhu, Mark Craven:
Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. TREC 2006 - [p1]