Nathan Srebro

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Behnam NeyshaburMember of Technical Staff, AnthropicPatvirtintas el. paštas anthropic.com
Shai Shalev-ShwartzThe Hebrew UniversityPatvirtintas el. paštas cs.huji.ac.il
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstitutePatvirtintas el. paštas ttic.edu
Ohad ShamirWeizmann Institute of SciencePatvirtintas el. paštas weizmann.ac.il
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPatvirtintas el. paštas cs.cmu.edu
Jason RennieSoftware Engieer, GooglePatvirtintas el. paštas google.com
Benjamin RechtProfessor of EECS, University of California, BerkeleyPatvirtintas el. paštas eecs.berkeley.edu
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoPatvirtintas el. paštas ttic.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganPatvirtintas el. paštas umich.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityPatvirtintas el. paštas cs.cmu.edu
Xueyuan ZhouPhD, Computer Science, University of ChicagoPatvirtintas el. paštas cs.uchicago.edu
Rachel WardUT Austin / Microsoft ResearchPatvirtintas el. paštas math.utexas.edu
Erik DemaineProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)Patvirtintas el. paštas mbzuai.ac.ae
Andreas ArgyriouMicrosoft UKPatvirtintas el. paštas alum.mit.edu
Shai Ben-DavidUniverity of Waterloo, CanadaPatvirtintas el. paštas uwaterloo.ca
Deanna NeedellProfessor of Mathematics, UCLAPatvirtintas el. paštas cmc.edu
David KargerProfessor of Computer Science, MITPatvirtintas el. paštas mit.edu
Sam Roweis (Memorial)New York UniversityPatvirtintas el. paštas buffalo.edu

Stebėti

Nathan Srebro

Professor, TTIC and University of Chicago

Patvirtintas el. paštas ttic.edu

Machine Learning Statistical Learning Theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Equality of opportunity in supervised learning M Hardt, E Price, N Srebro Advances in neural information processing systems 29, 2016	5379	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	2896	2007
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	1537	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	1381	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
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	1053	2018
Weighted low-rank approximations N Srebro, T Jaakkola Proceedings of the 20th international conference on machine learning (ICML …, 2003	1048	2003
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	768	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	736	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	715	2017
Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on learning theory, 1376-1401, 2015	688	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	651	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	590	2010
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in neural information processing systems 30, 2017	572	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	502	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	476	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	466	2016
Learning non-discriminatory predictors B Woodworth, S Gunasekar, MI Ohannessian, N Srebro Conference on learning theory, 1920-1953, 2017	459	2017

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