Nathan Srebro

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Coauteurs

Shai Shalev-ShwartzThe Hebrew UniversityAdresse e-mail validée de cs.huji.ac.il
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteAdresse e-mail validée de ttic.edu
Ohad ShamirWeizmann Institute of ScienceAdresse e-mail validée de weizmann.ac.il
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityAdresse e-mail validée de princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUAdresse e-mail validée de cs.cmu.edu
Jason RennieSoftware Engieer, GoogleAdresse e-mail validée de google.com
Benjamin RechtProfessor of EECS, University of California, BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoAdresse e-mail validée de ttic.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganAdresse e-mail validée de umich.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoAdresse e-mail validée de cs.uchicago.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Rachel WardUT Austin / Microsoft ResearchAdresse e-mail validée de math.utexas.edu
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)Adresse e-mail validée de mbzuai.ac.ae
Andreas ArgyriouMicrosoft UKAdresse e-mail validée de alum.mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaAdresse e-mail validée de uwaterloo.ca
David KargerProfessor of Computer Science, MITAdresse e-mail validée de mit.edu
Deanna NeedellProfessor of Mathematics, UCLAAdresse e-mail validée de cmc.edu
Sam Roweis (Memorial)New York UniversityAdresse e-mail validée de buffalo.edu
Sivan SabatoMcMaster University; Canada CIFAR AI Chair, Vector InstituteAdresse e-mail validée de mcmaster.ca

Suivre

Nathan Srebro

Professor, TTIC and University of Chicago

Adresse e-mail validée de ttic.edu

Machine Learning Statistical Learning Theory


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 2016	5119	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	2893	2007
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	1468	2017
Maximum-margin matrix factorization N Srebro, J Rennie, T Jaakkola Advances in neural information processing systems 17, 2004	1433	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	1343	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	1319	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th international conference on machine learning (ICML …, 2003	1045	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	1024	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	742	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	714	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	686	2017
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on learning theory, 1376-1401, 2015	663	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	621	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	566	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	489	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	476	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	463	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	450	2017

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