Nathan Srebro
Nathan Srebro
Professor, TTIC and University of Chicago
Verified email at ttic.edu
Title
Cited by
Cited by
Year
Pegasos: Primal estimated sub-gradient solver for svm
S Shalev-Shwartz, Y Singer, N Srebro, A Cotter
Mathematical programming 127 (1), 3-30, 2011
23962011
Equality of opportunity in supervised learning
M Hardt, E Price, N Srebro
arXiv preprint arXiv:1610.02413, 2016
13022016
Maximum-margin matrix factorization
N Srebro, J Rennie, TS Jaakkola
Advances in neural information processing systems, 1329-1336, 2005
12012005
Fast maximum margin matrix factorization for collaborative prediction
JDM Rennie, N Srebro
Proceedings of the 22nd international conference on Machine learning, 713-719, 2005
11482005
Weighted low-rank approximations
N Srebro, T Jaakkola
Proceedings of the 20th International Conference on Machine Learning (ICML†…, 2003
8802003
The marginal value of adaptive gradient methods in machine learning
AC Wilson, R Roelofs, M Stern, N Srebro, B Recht
arXiv preprint arXiv:1705.08292, 2017
6012017
Exploring generalization in deep learning
B Neyshabur, S Bhojanapalli, D McAllester, N Srebro
arXiv preprint arXiv:1706.08947, 2017
4852017
Rank, trace-norm and max-norm
N Srebro, A Shraibman
International Conference on Computational Learning Theory, 545-560, 2005
3782005
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
3092008
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
3082018
Communication-efficient distributed optimization using an approximate newton-type method
O Shamir, N Srebro, T Zhang
International conference on machine learning, 1000-1008, 2014
2932014
Learnability, stability and uniform convergence
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
The Journal of Machine Learning Research 11, 2635-2670, 2010
2882010
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
2862016
Norm-based capacity control in neural networks
B Neyshabur, R Tomioka, N Srebro
Conference on Learning Theory, 1376-1401, 2015
2862015
Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm
D Needell, N Srebro, R Ward
arXiv preprint arXiv:1310.5715, 2013
2792013
Better mini-batch algorithms via accelerated gradient methods
A Cotter, O Shamir, N Srebro, K Sridharan
arXiv preprint arXiv:1106.4574, 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
2592017
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
2432014
Collaborative filtering in a non-uniform world: Learning with the weighted trace norm
R Salakhutdinov, N Srebro
arXiv preprint arXiv:1002.2780, 2010
2362010
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