Nathan Srebro
Nathan Srebro
Professor, TTIC and University of Chicago
Adresse e-mail validée de ttic.edu
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Pegasos: Primal estimated sub-gradient solver for svm
S Shalev-Shwartz, Y Singer, N Srebro, A Cotter
Mathematical programming 127 (1), 3-30, 2011
23562011
Equality of opportunity in supervised learning
M Hardt, E Price, N Srebro
Advances in neural information processing systems, 3315-3323, 2016
12952016
Maximum-margin matrix factorization
N Srebro, J Rennie, T Jaakkola
Advances in neural information processing systems 17, 1329-1336, 2004
11922004
Fast maximum margin matrix factorization for collaborative prediction
JDM Rennie, N Srebro
Proceedings of the 22nd international conference on Machine learning, 713-719, 2005
11292005
Weighted low-rank approximations
N Srebro, T Jaakkola
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
8652003
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, 4148-4158, 2017
5962017
Exploring generalization in deep learning
B Neyshabur, S Bhojanapalli, D McAllester, N Srebro
Advances in neural information processing systems, 5947-5956, 2017
4762017
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
D Needell, N Srebro, R Ward
Mathematical Programming 155 (1-2), 549-573, 2016
4012016
Rank, trace-norm and max-norm
N Srebro, A Shraibman
International Conference on Computational Learning Theory, 545-560, 2005
3822005
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
3592007
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
3042008
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
2952018
Communication-efficient distributed optimization using an approximate newton-type method
O Shamir, N Srebro, T Zhang
International conference on machine learning, 1000-1008, 2014
2942014
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
2872016
Norm-based capacity control in neural networks
B Neyshabur, R Tomioka, N Srebro
Conference on Learning Theory, 1376-1401, 2015
2872015
Learnability, stability and uniform convergence
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
The Journal of Machine Learning Research 11, 2635-2670, 2010
2872010
Better mini-batch algorithms via accelerated gradient methods
A Cotter, O Shamir, N Srebro, K Sridharan
Advances in neural information processing systems, 1647-1655, 2011
2682011
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
B Neyshabur, S Bhojanapalli, N Srebro
arXiv preprint arXiv:1707.09564, 2017
2622017
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
2392014
Stochastic Convex Optimization.
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
COLT, 2009
2392009
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