Shai Shalev-Shwartz

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Amnon ShashuaProfessor of Computer Science, the Hebrew University of JerusalemVerified email at cs.huji.ac.il
Ohad ShamirWeizmann Institute of ScienceVerified email at weizmann.ac.il
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Tong ZhangHKUSTVerified email at tongzhang-ml.org
Ofer DekelMicrosoft ResearchVerified email at microsoft.com
Joseph (Yossi) KeshetProfessor, Dept. of Computer Science, Bar-Ilan UniversityVerified email at cs.biu.ac.il
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Amit DanielyHebrew UniversityVerified email at mail.huji.ac.il
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Alon GonenOrCamVerified email at orcam.com
Sham M KakadeUniversity of WashingtonVerified email at cs.washington.edu
Roi LivniTel Aviv UniversityVerified email at tauex.tau.ac.il
John DuchiAssistant Professor of Statistics and Electrical Engineering, Stanford UniversityVerified email at stanford.edu
Sivan SabatoBen Gurion University of the NegevVerified email at cs.bgu.ac.il
Amir GlobersonTel Aviv UniversityVerified email at post.tau.ac.il
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di MilanoVerified email at unimi.it
Tushar ChandraGoogleVerified email at google.com
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at cs.princeton.edu
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu

Shai Shalev-Shwartz

The Hebrew University

Verified email at cs.huji.ac.il - Homepage

Machine Learning Learning Theory Online Learning


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Understanding Machine Learning: From Theory to Algorithms S Shalev-Shwartz, S Ben-David Cambridge University Press, 2014	3645	2014
Understanding Machine Learning: From Theory to Algorithms S Shalev-Shwartz, S Ben-David Cambridge University Press, 2014	3645	2014
Pegasos: Primal estimated sub-gradient solver for svm S Shalev-Shwartz, Y Singer, N Srebro, A Cotter Mathematical programming 127 (1), 3-30, 2011	2459	2011
Online passive aggressive algorithms K Crammer, O Dekel, J Keshet, S Shalev-Shwartz, Y Singer	2122	2006
Online learning and online convex optimization S Shalev-Shwartz Foundations and trends in Machine Learning 4 (2), 107-194, 2011	1634	2011
Efficient projections onto the l₁-ball for learning in high dimensions J Duchi, S Shalev-Shwartz, Y Singer, T Chandra Proceedings of the 25th international conference on Machine learning, 272-279, 2008	1347	2008
Stochastic dual coordinate ascent methods for regularized loss minimization. S Shalev-Shwartz, T Zhang Journal of Machine Learning Research 14 (2), 2013	1015	2013
Stochastic Methods for l₁-regularized Loss Minimization S Shalev-Shwartz, A Tewari The Journal of Machine Learning Research 12, 1865-1892, 2011	456	2011
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization S Shalev-Shwartz, T Zhang International conference on machine learning, 64-72, 2014	450	2014
Safe, multi-agent, reinforcement learning for autonomous driving S Shalev-Shwartz, S Shammah, A Shashua arXiv preprint arXiv:1610.03295, 2016	414	2016
On the computational efficiency of training neural networks R Livni, S Shalev-Shwartz, O Shamir arXiv preprint arXiv:1410.1141, 2014	407	2014
Online and batch learning of pseudo-metrics S Shalev-Shwartz, Y Singer, AY Ng Proceedings of the twenty-first international conference on Machine learning, 94, 2004	398	2004
On a formal model of safe and scalable self-driving cars S Shalev-Shwartz, S Shammah, A Shashua arXiv preprint arXiv:1708.06374, 2017	368	2017
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan The Journal of Machine Learning Research 11, 2635-2670, 2010	328	2010
Composite Objective Mirror Descent. JC Duchi, S Shalev-Shwartz, Y Singer, A Tewari COLT, 14-26, 2010	321	2010
SVM optimization: inverse dependence on training set size S Shalev-Shwartz, N Srebro Proceedings of the 25th international conference on Machine learning, 928-935, 2008	313	2008
Online learning: Theory, algorithms, and applications S Shalev-Shwartz, Y Singer	284	2007
Stochastic Convex Optimization. S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan COLT 2 (4), 5, 2009	265	2009
The forgetron: A kernel-based perceptron on a budget O Dekel, S Shalev-Shwartz, Y Singer SIAM Journal on Computing 37 (5), 1342-1372, 2008	212	2008
Decoupling" when to update" from" how to update" E Malach, S Shalev-Shwartz arXiv preprint arXiv:1706.02613, 2017	199	2017

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