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Nathan Srebro
Nathan Srebro
Professor, TTIC and University of Chicago
Verified email at ttic.edu
Title
Cited by
Cited by
Year
Equality of opportunity in supervised learning
M Hardt, E Price, N Srebro
Advances in neural information processing systems 29, 2016
38672016
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
27712007
Maximum-margin matrix factorization
N Srebro, J Rennie, T Jaakkola
Advances in neural information processing systems 17, 2004
13862004
Fast maximum margin matrix factorization for collaborative prediction
JDM Rennie, N Srebro
Proceedings of the 22nd international conference on Machine learning, 713-719, 2005
12782005
Exploring generalization in deep learning
B Neyshabur, S Bhojanapalli, D McAllester, N Srebro
Advances in neural information processing systems 30, 2017
11882017
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
11532017
Weighted low-rank approximations
N Srebro, T Jaakkola
Proceedings of the 20th international conference on machine learning (ICML†…, 2003
10032003
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
8322018
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
D Needell, R Ward, N Srebro
Advances in neural information processing systems 27, 2014
6262014
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
6142014
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
B Neyshabur, S Bhojanapalli, N Srebro
arXiv preprint arXiv:1707.09564, 2017
5832017
Norm-based capacity control in neural networks
B Neyshabur, R Tomioka, N Srebro
Conference on learning theory, 1376-1401, 2015
5672015
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
5412018
Learnability, stability and uniform convergence
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
The Journal of Machine Learning Research 11, 2635-2670, 2010
4862010
Rank, trace-norm and max-norm
N Srebro, A Shraibman
International conference on computational learning theory, 545-560, 2005
4682005
Implicit regularization in matrix factorization
S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro
Advances in neural information processing systems 30, 2017
4342017
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
4122016
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
4002007
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
3782018
Learning non-discriminatory predictors
B Woodworth, S Gunasekar, MI Ohannessian, N Srebro
Conference on Learning Theory, 1920-1953, 2017
3752017
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