Koby Crammer

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Fernando PereiraVP, GoogleVerified email at google.com
Mark DredzeJohns Hopkins UniversityVerified email at cs.jhu.edu
Alex KuleszaResearch Scientist, GoogleVerified email at google.com
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Partha TalukdarGoogle Research and IISc BangaloreVerified email at iisc.ac.in
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Joseph KeshetProfessor, Faculty of Electrical & Computer Engineering, TechnionVerified email at technion.ac.il
Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Ofer DekelMicrosoft ResearchVerified email at microsoft.com
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ NvidiaVerified email at technion.ac.il
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Slobodan VuceticProfessor of Computer Science, Temple UniversityVerified email at temple.edu
Hadas BenistyTechnionVerified email at technion.ac.il
Ran El-YanivProfessor of Computer Science, Technion - Israel Institute of Technology. Chief Scientist - Deci AIVerified email at cs.technion.ac.il
David MalahProfessor Emeritus, Electrical and Computer Engineering, Technion - I.I.TVerified email at ee.technion.ac.il
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Yevgeny SeldinProfessor, Department of Computer Science, University of CopenhagenVerified email at di.ku.dk
Kuzman GanchevGoogle IncVerified email at google.com
Francesco OrabonaAssociate Professor, KAUSTVerified email at orabona.com
Axel BernalData Scientist, Human Longevity IncVerified email at humanlongevity.com

Koby Crammer

Professor of Electrical Engineering, Technion - Israel Institute of Technology

Verified email at ee.technion.ac.il

Machine Learning Big Data


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A theory of learning from different domains S Ben-David, J Blitzer, K Crammer, A Kulesza, F Pereira, JW Vaughan Machine learning 79 (1-2), 151-175, 2010	3920	2010
On the algorithmic implementation of multiclass kernel-based vector machines K Crammer, Y Singer Journal of machine learning research 2 (Dec), 265-292, 2001	3028	2001
Analysis of representations for domain adaptation S Ben-David, J Blitzer, K Crammer, F Pereira Advances in neural information processing systems, 137-144, 2007	2663	2007
Online passive-aggressive algorithms K Crammer, O Dekel, J Keshet, S Shalev-Shwartz, Y Singer Journal of Machine Learning Research 7 (Mar), 551-585, 2006	2621	2006
Online large-margin training of dependency parsers R McDonald, K Crammer, F Pereira Proceedings of the 43rd annual meeting on association for computational …, 2005	1034	2005
On the learnability and design of output codes for multiclass problems K Crammer, Y Singer Machine learning 47 (2-3), 201-233, 2002	1000	2002
Pranking with ranking K Crammer, Y Singer Advances in neural information processing systems, 641-647, 2002	902	2002
Ultraconservative online algorithms for multiclass problems K Crammer, Y Singer Journal of Machine Learning Research 3 (Jan), 951-991, 2003	772	2003
Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2008	598	2008
Confidence-weighted linear classification M Dredze, K Crammer, F Pereira Proceedings of the 25th international conference on Machine learning, 264-271, 2008	584	2008
Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Advances in neural information processing systems, 414-422, 2009	398	2009
Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008	336	2008
Margin analysis of the LVQ algorithm K Crammer, R Gilad-Bachrach, A Navot, N Tishby Advances in neural information processing systems, 479-486, 2003	325	2003
New regularized algorithms for transductive learning PP Talukdar, K Crammer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009	300	2009
A family of additive online algorithms for category ranking K Crammer, Y Singer Journal of Machine Learning Research 3 (Feb), 1025-1058, 2003	261	2003
Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale svm training Z Wang, K Crammer, S Vucetic Journal of Machine Learning Research 13 (Oct), 3103-3131, 2012	253	2012
Robust support vector machine training via convex outlier ablation L Xu, K Crammer, D Schuurmans AAAI 6, 536-542, 2006	216	2006
Online classification on a budget K Crammer, J Kandola, Y Singer Advances in neural information processing systems, 225-232, 2004	212	2004
Exact convex confidence-weighted learning K Crammer, M Dredze, F Pereira Advances in Neural Information Processing Systems 21, 2008	190	2008
Kernel design using boosting K Crammer, J Keshet, Y Singer Advances in neural information processing systems, 553-560, 2003	185	2003

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