Nathan Srebro - Google Scholar Citations

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Citations	15737	10834
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Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Behnam NeyshaburSr. Research Scientist, GoogleVerified email at google.com
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Ohad ShamirWeizmann Institute of ScienceVerified email at weizmann.ac.il
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Jason RennieSoftware Engieer, GoogleVerified email at google.com
Jason D. LeeAssistant Professor of Electrical Engineering, Princeton UniversityVerified email at princeton.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Maria-Florina BalcanAssociate Professor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Ambuj Tewari (अम्बुज तिवारी)Associate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyVerified email at mit.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoVerified email at cs.uchicago.edu
Martin TakáčLehigh UniversityVerified email at lehigh.edu
Deanna NeedellProfessor of Mathematics, UCLAVerified email at cmc.edu
Rachel WardUT AustinVerified email at math.utexas.edu
Andreas ArgyriouMicrosoft UKVerified email at alum.mit.edu
David KargerProfessor of Computer Science, MITVerified email at mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Sam Roweis (Memorial)New York UniversityVerified email at buffalo.edu

Nathan Srebro

Professor, TTIC and University of Chicago

Verified email at ttic.edu

Machine Learning Statistical Learning Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Pegasos: Primal estimated sub-gradient solver for svm S Shalev-Shwartz, Y Singer, N Srebro, A Cotter Mathematical programming 127 (1), 3-30, 2011	2190	2011
Maximum-margin matrix factorization N Srebro, J Rennie, TS Jaakkola Advances in neural information processing systems, 1329-1336, 2005	1108	2005
Fast maximum margin matrix factorization for collaborative prediction JDM Rennie, N Srebro Proceedings of the 22nd international conference on Machine learning, 713-719, 2005	1056	2005
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems, 3315-3323, 2016	858	2016
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003	807	2003
The marginal value of adaptive gradient methods in machine learning AC Wilson, R Roelofs, M Stern, N Srebro, B Recht Advances in Neural Information Processing Systems, 4148-4158, 2017	401	2017
Rank, trace-norm and max-norm N Srebro, A Shraibman International Conference on Computational Learning Theory, 545-560, 2005	343	2005
Uncovering shared structures in multiclass classification Y Amit, M Fink, N Srebro, S Ullman Proceedings of the 24th international conference on Machine learning, 17-24, 2007	334	2007
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in Neural Information Processing Systems, 5947-5956, 2017	314	2017
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	282	2008
Global optimality of local search for low rank matrix recovery S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 3873-3881, 2016	244	2016
Better mini-batch algorithms via accelerated gradient methods A Cotter, O Shamir, N Srebro, K Sridharan Advances in neural information processing systems, 1647-1655, 2011	239	2011
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan Journal of Machine Learning Research 11 (Oct), 2635-2670, 2010	236	2010
Communication-efficient distributed optimization using an approximate newton-type method O Shamir, N Srebro, T Zhang International conference on machine learning, 1000-1008, 2014	233	2014
Learning with matrix factorizations N Srebro	225	2004
Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm D Needell, R Ward, N Srebro Advances in neural information processing systems, 1017-1025, 2014	221	2014
Collaborative filtering in a non-uniform world: Learning with the weighted trace norm N Srebro, RR Salakhutdinov Advances in Neural Information Processing Systems, 2056-2064, 2010	213	2010
A theory of learning with similarity functions MF Balcan, A Blum, N Srebro Machine Learning 72 (1-2), 89-112, 2008	205	2008
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376-1401, 2015	195	2015
Stochastic Convex Optimization. S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan COLT, 2009	194	2009

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