Nathan Srebro

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Shai Shalev-ShwartzThe Hebrew UniversityDirección de correo verificada de cs.huji.ac.il
Behnam NeyshaburMember of Technical Staff, AnthropicDirección de correo verificada de anthropic.com
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteDirección de correo verificada de ttic.edu
Ohad ShamirWeizmann Institute of ScienceDirección de correo verificada de weizmann.ac.il
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityDirección de correo verificada de princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUDirección de correo verificada de cs.cmu.edu
Jason RennieSoftware Engieer, GoogleDirección de correo verificada de google.com
Benjamin RechtProfessor of EECS, University of California, BerkeleyDirección de correo verificada de eecs.berkeley.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoDirección de correo verificada de ttic.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganDirección de correo verificada de umich.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoDirección de correo verificada de cs.uchicago.edu
Rachel WardUT Austin / Microsoft ResearchDirección de correo verificada de math.utexas.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)Dirección de correo verificada de mbzuai.ac.ae
Andreas ArgyriouMicrosoft UKDirección de correo verificada de alum.mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaDirección de correo verificada de uwaterloo.ca
Deanna NeedellProfessor of Mathematics, UCLADirección de correo verificada de cmc.edu
David KargerProfessor of Computer Science, MITDirección de correo verificada de mit.edu
Sam Roweis (Memorial)New York UniversityDirección de correo verificada de buffalo.edu

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Nathan Srebro

Professor, TTIC and University of Chicago

Dirección de correo verificada de ttic.edu

Machine Learning Statistical Learning Theory


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 2016	5323	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	2909	2007
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	1517	2017
Maximum-margin matrix factorization N Srebro, J Rennie, T Jaakkola Advances in neural information processing systems 17, 2004	1437	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	1378	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	1325	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th international conference on machine learning (ICML …, 2003	1052	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	1051	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	754	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	733	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	705	2017
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on learning theory, 1376-1401, 2015	676	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	636	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	584	2010
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in neural information processing systems 30, 2017	570	2017
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	495	2018
Rank, trace-norm and max-norm N Srebro, A Shraibman International conference on computational learning theory, 545-560, 2005	490	2005
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	472	2018
Learning non-discriminatory predictors B Woodworth, S Gunasekar, MI Ohannessian, N Srebro Conference on Learning Theory, 1920-1953, 2017	460	2017
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	460	2016

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