Nathan Srebro

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Shai Shalev-ShwartzThe Hebrew UniversityVerified email at cs.huji.ac.il
Behnam NeyshaburStaff 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 D. LeeAssistant Professor of Electrical Engineering, Princeton UniversityVerified email at princeton.edu
Jason RennieSoftware Engieer, GoogleVerified email at google.com
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Ambuj Tewari | अम्बुज तिवारीAssociate 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
Deanna NeedellProfessor of Mathematics, UCLAVerified email at cmc.edu
Rachel WardUT AustinVerified email at math.utexas.edu
Martin TakáčMohamed Bin Zayed University of Artificial Intelligence / Lehigh UniversityVerified email at lehigh.edu
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
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	2465	2011
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 3315-3323, 2016	1704	2016
Maximum-Margin Matrix Factorization. N Srebro, JDM Rennie, TS Jaakkola NIPS 17, 1329-1336, 2004	1245	2004
Fast maximum margin matrix factorization for collaborative prediction JDM Rennie, N Srebro Proceedings of the 22nd international conference on Machine learning, 713-719, 2005	1182	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003	904	2003
The marginal value of adaptive gradient methods in machine learning AC Wilson, R Roelofs, M Stern, N Srebro, B Recht arXiv preprint arXiv:1705.08292, 2017	716	2017
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro arXiv preprint arXiv:1706.08947, 2017	616	2017
Rank, trace-norm and max-norm N Srebro, A Shraibman International Conference on Computational Learning Theory, 545-560, 2005	398	2005
The implicit bias of gradient descent on separable data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro The Journal of Machine Learning Research 19 (1), 2822-2878, 2018	391	2018
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	375	2007
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376-1401, 2015	335	2015
Learnability, stability and uniform convergence S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan The Journal of Machine Learning Research 11, 2635-2670, 2010	331	2010
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks B Neyshabur, S Bhojanapalli, N Srebro arXiv preprint arXiv:1707.09564, 2017	321	2017
Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm D Needell, R Ward, N Srebro Advances in neural information processing systems 27, 1017-1025, 2014	316	2014
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	315	2016
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
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	306	2014
Better mini-batch algorithms via accelerated gradient methods A Cotter, O Shamir, N Srebro, K Sridharan arXiv preprint arXiv:1106.4574, 2011	296	2011
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	283*	2018
Stochastic Convex Optimization. S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan COLT 2 (4), 5, 2009	266	2009

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