Nathan Srebro

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Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Behnam NeyshaburSenior Staff Research Scientist, DeepMindVerified 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
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Jason RennieSoftware Engieer, GoogleVerified email at google.com
Benjamin RechtProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerified email at umich.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoVerified email at cs.uchicago.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyVerified email at mit.edu
Rachel WardUT Austin / Microsoft ResearchVerified email at math.utexas.edu
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at mbzuai.ac.ae
Andreas ArgyriouMicrosoft UKVerified email at alum.mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
David KargerProfessor of Computer Science, MITVerified email at mit.edu
Deanna NeedellProfessor of Mathematics, UCLAVerified email at cmc.edu
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
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 2016	5075	2016
Pegasos: Primal estimated sub-gradient solver for svm S Shalev-Shwartz, Y Singer, N Srebro Proceedings of the 24th international conference on Machine learning, 807-814, 2007	2887	2007
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	1461	2017
Maximum-margin matrix factorization N Srebro, J Rennie, T Jaakkola Advances in neural information processing systems 17, 2004	1431	2004
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 30, 2017	1330	2017
Fast maximum margin matrix factorization for collaborative prediction JDM Rennie, N Srebro Proceedings of the 22nd international conference on Machine learning, 713-719, 2005	1318	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th international conference on machine learning (ICML …, 2003	1039	2003
The implicit bias of gradient descent on separable data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro Journal of Machine Learning Research 19 (70), 1-57, 2018	1022	2018
In search of the real inductive bias: On the role of implicit regularization in deep learning B Neyshabur, R Tomioka, N Srebro arXiv preprint arXiv:1412.6614, 2014	739	2014
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm D Needell, R Ward, N Srebro Advances in neural information processing systems 27, 2014	710	2014
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks B Neyshabur, S Bhojanapalli, N Srebro arXiv preprint arXiv:1707.09564, 2017	684	2017
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on learning theory, 1376-1401, 2015	661	2015
Towards understanding the role of over-parametrization in generalization of neural networks B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro arXiv preprint arXiv:1805.12076, 2018	616	2018
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan The Journal of Machine Learning Research 11, 2635-2670, 2010	561	2010
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in neural information processing systems 30, 2017	556	2017
Rank, trace-norm and max-norm N Srebro, A Shraibman International conference on computational learning theory, 545-560, 2005	488	2005
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	475	2018
Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in neural information processing systems 31, 2018	460	2018
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	459	2016
Learning non-discriminatory predictors B Woodworth, S Gunasekar, MI Ohannessian, N Srebro Conference on Learning Theory, 1920-1953, 2017	449	2017

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