Nathan Srebro

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Shai Shalev-ShwartzThe Hebrew UniversityE-mail confirmado em cs.huji.ac.il
Behnam NeyshaburSenior Staff Research Scientist, GoogleE-mail confirmado em google.com
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteE-mail confirmado em ttic.edu
Ohad ShamirWeizmann Institute of ScienceE-mail confirmado em weizmann.ac.il
Jason D. LeeAssistant Professor of Electrical Engineering and Computer Science , Princeton UniversityE-mail confirmado em princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUE-mail confirmado em cs.cmu.edu
Jason RennieSoftware Engieer, GoogleE-mail confirmado em google.com
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyE-mail confirmado em eecs.berkeley.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoE-mail confirmado em ttic.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityE-mail confirmado em cs.cmu.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganE-mail confirmado em umich.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyE-mail confirmado em mit.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoE-mail confirmado em cs.uchicago.edu
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)E-mail confirmado em mbzuai.ac.ae
Andreas ArgyriouMicrosoft UKE-mail confirmado em alum.mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaE-mail confirmado em uwaterloo.ca
David KargerProfessor of Computer Science, MITE-mail confirmado em mit.edu
Sam Roweis (Memorial)New York UniversityE-mail confirmado em buffalo.edu
Deanna NeedellProfessor of Mathematics, UCLAE-mail confirmado em cmc.edu
Rachel WardUT AustinE-mail confirmado em math.utexas.edu

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Nathan Srebro

Professor, TTIC and University of Chicago

E-mail confirmado em ttic.edu

Machine Learning Statistical Learning Theory


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Pegasos: Primal estimated sub-gradient solver for svm S Shalev-Shwartz, Y Singer, N Srebro, A Cotter Mathematical programming 127 (1), 3-30, 2011	2572	2011
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 2016	2493	2016
Maximum-margin matrix factorization N Srebro, J Rennie, T Jaakkola Advances in neural information processing systems 17, 2004	1300	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	1201	2005
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th international conference on machine learning (ICML …, 2003	950	2003
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	887	2017
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	826	2017
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	546	2018
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm D Needell, R Ward, N Srebro Advances in neural information processing systems 27, 2014	518	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	419	2017
Rank, trace-norm and max-norm N Srebro, A Shraibman International conference on computational learning theory, 545-560, 2005	418	2005
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	413	2014
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376-1401, 2015	410	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	389	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	381	2010
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	381	2007
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	352	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	318	2008
Better mini-batch algorithms via accelerated gradient methods A Cotter, O Shamir, N Srebro, K Sridharan Advances in neural information processing systems 24, 2011	315	2011
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems 30, 2017	282	2017

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