Nathan Srebro - Google Scholar Citations

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	All	Since 2014
Citations	13431	9729
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Co-authors

Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Behnam NeyshaburSenior 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 SalakhutdinovAssociate Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Jason RennieSoftware Engieer, GoogleVerified email at google.com
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Jason D. LeeAssistant Professor of Electrical Engineering, Princeton UniversityVerified email at princeton.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 TewariAssociate 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
Andreas ArgyriouMicrosoft UKVerified email at alum.mit.edu
Deanna NeedellProfessor of Mathematics, UCLAVerified email at cmc.edu
Rachel WardUT AustinVerified email at math.utexas.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	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	2048	2011
Maximum-margin matrix factorization N Srebro, J Rennie, TS Jaakkola Advances in neural information processing systems, 1329-1336, 2005	1041	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	993	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003	764	2003
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems, 3315-3323, 2016	572	2016
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	316	2007
Rank, trace-norm and max-norm N Srebro, A Shraibman International Conference on Computational Learning Theory, 545-560, 2005	311	2005
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	270	2008
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	262	2017
Learning with matrix factorizations N Srebro	208	2004
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan Journal of Machine Learning Research 11 (Oct), 2635-2670, 2010	206	2010
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	201	2011
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	200	2016
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in Neural Information Processing Systems, 5947-5956, 2017	199	2017
A theory of learning with similarity functions MF Balcan, A Blum, N Srebro Machine Learning 72 (1-2), 89-112, 2008	190	2008
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	183	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	177	2014
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	174	2014
Stochastic Convex Optimization. S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan COLT, 2009	167	2009
Mini-Batch Primal and Dual Methods for SVMs. M Takác, AS Bijral, P Richtárik, N Srebro ICML (3), 1022-1030, 2013	157	2013

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