Lawrence K Saul

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Geoffrey M. VoelkerUniversity of California, San DiegoVerified email at cs.ucsd.edu
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu
Stefan SavageProfessor of Computer Science and Engineering, UC San DiegoVerified email at cs.ucsd.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Daniel D. LeeTisch University Professor of ECE, Cornell Tech and Executive Vice President, Samsung ResearchVerified email at alum.mit.edu
John BlitzerGoogle ResearchVerified email at google.com
Diane HuEtsyVerified email at etsy.com
Fernando PereiraVP and Engineering Fellow, GoogleVerified email at google.com
Jont B AllenUniversity of IL, Professor of ECEVerified email at illinois.edu
Yun MaoFacebookVerified email at fb.com
John Ashley BurgoyneVerified email at uva.nl
Yuanqing LinAibeeVerified email at aibee.co
Do-kyum KimPhD Student of Computer Science, UCSDVerified email at cs.ucsd.edu
Yuanyuan ZhouUniversity of California, San DiegoVerified email at eng.ucsd.edu
Peng HuangJohns Hopkins UniversityVerified email at cs.jhu.edu
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Laurens van der MaatenResearch Director, Facebook AI ResearchVerified email at fb.com
Ian D. FosterUC San DiegoVerified email at eng.ucsd.edu
Andrew I. ScheinWaymo - Behavior and PlanningVerified email at andrewschein.com

Lawrence K Saul

Professor of Computer Science and Engineering, UC San Diego

Verified email at cs.ucsd.edu - Homepage

Machine learning high dimensional data analysis


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Nonlinear dimensionality reduction by locally linear embedding ST Roweis, LK Saul science 290 (5500), 2323-2326, 2000	16737	2000
An introduction to variational methods for graphical models MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul Machine learning 37 (2), 183-233, 1999	4489	1999
Distance metric learning for large margin nearest neighbor classification. KQ Weinberger, LK Saul Journal of machine learning research 10 (2), 2009	3900	2009
Distance metric learning for large margin nearest neighbor classification KQ Weinberger, J Blitzer, LK Saul Advances in neural information processing systems, 1473-1480, 2006	2297	2006
Think globally, fit locally: unsupervised learning of low dimensional manifolds LK Saul, ST Roweis Departmental Papers (CIS), 12, 2003	2112	2003
Unsupervised learning of image manifolds by semidefinite programming KQ Weinberger, LK Saul International journal of computer vision 70 (1), 77-90, 2006	1153	2006
Beyond blacklists: learning to detect malicious web sites from suspicious URLs J Ma, LK Saul, S Savage, GM Voelker Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	968	2009
Identifying suspicious URLs: an application of large-scale online learning J Ma, LK Saul, S Savage, GM Voelker Proceedings of the 26th annual international conference on machine learning …, 2009	664	2009
Kernel methods for deep learning Y Cho University of California, San Diego, 2012	632	2012
Learning a kernel matrix for nonlinear dimensionality reduction KQ Weinberger, F Sha, LK Saul Proceedings of the twenty-first international conference on Machine learning …, 2004	607	2004
Mean field theory for sigmoid belief networks LK Saul, T Jaakkola, MI Jordan Journal of artificial intelligence research 4, 61-76, 1996	535	1996
Fast solvers and efficient implementations for distance metric learning KQ Weinberger, LK Saul Proceedings of the 25th international conference on Machine learning, 1160-1167, 2008	364	2008
An introduction to nonlinear dimensionality reduction by maximum variance unfolding KQ Weinberger, LK Saul AAAI 6, 1683-1686, 2006	327	2006
Exploiting tractable substructures in intractable networks LK Saul, MI Jordan Advances in neural information processing systems, 486-492, 1996	326	1996
Semisupervised alignment of manifolds J Ham, D Lee, L Saul International Workshop on Artificial Intelligence and Statistics, 120-127, 2005	322	2005
Spectral methods for dimensionality reduction. LK Saul, KQ Weinberger, F Sha, J Ham, DD Lee Semi-supervised learning 3, 2006	321	2006
Global coordination of local linear models ST Roweis, LK Saul, GE Hinton Advances in neural information processing systems 2, 889-896, 2002	290	2002
An introduction to locally linear embedding LK Saul, ST Roweis unpublished. Available at: http://www. cs. toronto. edu/~ roweis/lle …, 2000	268	2000
Mixed memory markov models: Decomposing complex stochastic processes as mixtures of simpler ones LK Saul, MI Jordan Machine learning 37 (1), 75-87, 1999	241	1999
Aggregate and mixed-order Markov models for statistical language processing L Saul, F Pereira arXiv preprint cmp-lg/9706007, 1997	241	1997

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