Brian Kulis

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Inderjit S. DhillonVice President/Distinguished Scientist at Amazon, and Professor, Computer Science, UT AustinVerified email at cs.utexas.edu
Prateek JainGoogle Research IndiaVerified email at google.com
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Kate SaenkoBoston University & MIT-IBM Watson AI LabVerified email at bu.edu
Suvrit SraMITVerified email at mit.edu
Kristen GraumanProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Mario FritzFaculty CISPA Helmholtz Center for Information Security; Professor Saarland UniversityVerified email at cispa.de
Xide XiaResearch Scientist at Meta AIVerified email at fb.com
Bart SelmanProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
John HopcroftCornell UniversityVerified email at cs.cornell.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ali SiahkamariPhD, Boston UniversityVerified email at bu.edu
Judy HoffmanAssistant Professor, Georgia TechVerified email at gatech.edu
Matyas A SustikUniversity of Texas at AustinVerified email at cs.utexas.edu
Rachel ManzelliBoston UniversityVerified email at bu.edu
Raymond MooneyProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Sugato BasuDistinguished Scientist at GoogleVerified email at google.com
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyVerified email at mit.edu
Trevor CampbellAssistant Professor, Statistics, UBCVerified email at stat.ubc.ca
Venkatesh SaligramaProfessor, Electrical and Computer Engineering, Boston UniversityVerified email at bu.edu

Brian Kulis

Associate Professor at Boston University & Amazon Scholar

Verified email at bu.edu - Homepage

Machine Learning Artificial Intelligence Computer Vision


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Information-theoretic metric learning JV Davis, B Kulis, P Jain, S Sra, IS Dhillon Proceedings of the 24th international conference on Machine learning, 209-216, 2007	2517	2007
Adapting visual category models to new domains K Saenko, B Kulis, M Fritz, T Darrell European conference on computer vision, 213-226, 2010	2354	2010
Kernel k-means: spectral clustering and normalized cuts IS Dhillon, Y Guan, B Kulis Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	1468	2004
Weighted graph cuts without eigenvectors a multilevel approach IS Dhillon, Y Guan, B Kulis IEEE transactions on pattern analysis and machine intelligence 29 (11), 1944 …, 2007	1122	2007
Kernelized locality-sensitive hashing for scalable image search B Kulis, K Grauman 2009 IEEE 12th international conference on computer vision, 2130-2137, 2009	1064	2009
Metric learning: A survey B Kulis Foundations and Trends® in Machine Learning 5 (4), 287-364, 2013	947	2013
Learning to hash with binary reconstructive embeddings B Kulis, T Darrell Advances in neural information processing systems 22, 2009	942	2009
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms B Kulis, K Saenko, T Darrell CVPR 2011, 1785-1792, 2011	797	2011
Semi-supervised graph clustering: a kernel approach B Kulis, S Basu, I Dhillon, R Mooney Machine learning 74 (1), 1-22, 2009	633	2009
Tracking evolving communities in large linked networks J Hopcroft, O Khan, B Kulis, B Selman Proceedings of the National Academy of Sciences 101 (suppl_1), 5249-5253, 2004	430	2004
Revisiting k-means: New algorithms via Bayesian nonparametrics B Kulis, MI Jordan arXiv preprint arXiv:1111.0352, 2011	399	2011
Kernelized locality-sensitive hashing B Kulis, K Grauman IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (6), 1092-1104, 2011	397	2011
Fast image search for learned metrics P Jain, B Kulis, K Grauman 2008 IEEE Conference on computer vision and pattern recognition, 1-8, 2008	346	2008
Fast similarity search for learned metrics B Kulis, P Jain, K Grauman IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12), 2143 …, 2009	303	2009
Low-Rank Kernel Learning with Bregman Matrix Divergences. B Kulis, MA Sustik, IS Dhillon Journal of Machine Learning Research 10 (2), 2009	277	2009
A unified view of kernel k-means, spectral clustering and graph cuts IS Dhillon, Y Guan, B Kulis Computer Science Department, University of Texas at Austin, 2004	259	2004
Online metric learning and fast similarity search P Jain, B Kulis, I Dhillon, K Grauman Advances in neural information processing systems 21, 2008	248	2008
Learning low-rank kernel matrices B Kulis, M Sustik, I Dhillon Proceedings of the 23rd international conference on Machine learning, 505-512, 2006	219	2006
Metric and kernel learning using a linear transformation P Jain, B Kulis, JV Davis, IS Dhillon The Journal of Machine Learning Research 13 (1), 519-547, 2012	215	2012
Discovering latent domains for multisource domain adaptation J Hoffman, B Kulis, T Darrell, K Saenko European Conference on Computer Vision, 702-715, 2012	206	2012

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