Xinhua Zhang

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Dale SchuurmansUniversity of Alberta, GoogleVerified email at cs.ualberta.ca
Yaoliang YuUniversity of WaterlooVerified email at uwaterloo.ca
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
Ankan SahaSenior Staff Applied Researcher, LinkedinVerified email at linkedin.com
Hongwei JinArgonne National LaboratoryVerified email at anl.gov
Zhan Shiuniversity of illinois at chicagoVerified email at uic.edu
Brian D ZiebartAssociate Professor of Computer Science, University of Illinois at ChicagoVerified email at uic.edu
Martha WhiteUniversity of AlbertaVerified email at ualberta.ca
Hao ChengMicrosoft ResearchVerified email at microsoft.com
Mohammad Ali BashiriUniversity of Illinois at ChicagoVerified email at uic.edu
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Alex SmolaBoson AIVerified email at smola.org
Le SongBiomap, Mohamed bin Zayed University of Artificial IntelligenceVerified email at biomap.com
Rizal FathonyGrabVerified email at cs.cmu.edu
Vignesh GanapathiramanAmazon, ML solutions LabVerified email at amazon.com
Wee Sun LeeProfessor, Department of Computer Science, National University of SingaporeVerified email at comp.nus.edu.sg
Shin MatsushimaThe University of TokyoVerified email at ds.itc.u-tokyo.ac.jp
Hyokun YunMachine Learning Scientist at AmazonVerified email at amazon.com
Yingyi MaMeta AIVerified email at fb.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de

Xinhua Zhang

University of Illinois Chicago

Verified email at uic.edu - Homepage

Machine Learning


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Convex multi-view subspace learning M White, Y Yu, X Zhang, D Schuurmans Advances in Neural Information Processing Systems, 1673-1681, 2012	191	2012
Accelerated training for matrix-norm regularization: A boosting approach X Zhang, Y Yu, D Schuurmans Advances in Neural Information Processing Systems, 2906-2914, 2012	109	2012
Support Vector Machines X Zhang Encyclopedia of Machine Learning, 941-946, 2011	95*	2011
Tailoring density estimation via reproducing kernel moment matching L Song, X Zhang, A Smola, A Gretton, B Schölkopf Proceedings of the 25th international conference on Machine learning, 992-999, 2008	77	2008
Hyperparameter learning for graph based semi-supervised learning algorithms X Zhang, WS Lee Advances in neural information processing systems, 1585-1592, 2006	72	2006
Generalized conditional gradient for sparse estimation Y Yu, X Zhang, D Schuurmans Journal of Machine Learning Research 18 (144), 1-46, 2017	51	2017
Bayesian online learning for multi-label and multi-variate performance measures X Zhang, T Graepel, R Herbrich Proceedings of the Thirteenth International Conference on Artificial …, 2010	51	2010
Latent adversarial training of graph convolution networks H Jin, X Zhang ICML Workshop on Learning and Reasoning with Graph-Structured Representations, 2019	46	2019
Kernel measures of independence for non-iid data X Zhang, L Song, A Gretton, A Smola Advances in neural information processing systems 21, 1937-1944, 2008	44	2008
Consistent image analogies using semi-supervised learning L Cheng, SVN Vishwanathan, X Zhang 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008	41	2008
Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. X Zhang, Y Yu, M White, R Huang, D Schuurmans AAAI, 2011	38	2011
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks H Jin, Z Shi, VJSA Peruri, X Zhang Advances in Neural Information Processing Systems 33, 2020	36	2020
Convex deep learning via normalized kernels Ö Aslan, X Zhang, D Schuurmans Advances in Neural Information Processing Systems, 3275-3283, 2014	36	2014
Smoothing multivariate performance measures X Zhang, A Saha, SVN Vishwanathan Journal of Machine Learning Research 13 (Dec), 3623-3680, 2012	36	2012
Scalable and Sound Low-Rank Tensor Learning H Cheng, Y Yu, X Zhang, E Xing, D Schuurmans Proceedings of the 19th International Conference on Artificial Intelligence …, 2016	32	2016
Conditional random fields for multi-agent reinforcement learning X Zhang, D Aberdeen, SVN Vishwanathan Proceedings of the 24th international conference on Machine learning, 1143-1150, 2007	31	2007
New approximation algorithms for minimum enclosing convex shapes A Saha, SVN Vishwanathan, X Zhang Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete …, 2011	30	2011
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction Z Shi, X Zhang, Y Yu Advances in Neural Information Processing Systems, 6033-6043, 2017	28	2017
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search MA Bashiri, X Zhang Advances in Neural Information Processing Systems, 2687-2697, 2017	27	2017
New approximation algorithms for minimum enclosing convex shapes A Saha, SVN Vishwanathan, X Zhang Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete …, 2011	27	2011

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