Nathan Srebro
Nathan Srebro
Professor, TTIC and University of Chicago
Potvrđena adresa e-pošte na ttic.edu
Naslov
Citirano
Citirano
Godina
Pegasos: Primal estimated sub-gradient solver for svm
S Shalev-Shwartz, Y Singer, N Srebro, A Cotter
Mathematical programming 127 (1), 3-30, 2011
24922011
Equality of opportunity in supervised learning
M Hardt, E Price, N Srebro
Advances in neural information processing systems 29, 3315-3323, 2016
18212016
Maximum-Margin Matrix Factorization.
N Srebro, JDM Rennie, TS Jaakkola
NIPS 17, 1329-1336, 2004
12562004
Fast maximum margin matrix factorization for collaborative prediction
JDM Rennie, N Srebro
Proceedings of the 22nd international conference on Machine learning, 713-719, 2005
11922005
Weighted low-rank approximations
N Srebro, T Jaakkola
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
9092003
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
7472017
Exploring generalization in deep learning
B Neyshabur, S Bhojanapalli, D McAllester, N Srebro
arXiv preprint arXiv:1706.08947, 2017
6442017
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
4102018
Rank, trace-norm and max-norm
N Srebro, A Shraibman
International Conference on Computational Learning Theory, 545-560, 2005
3992005
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
3762007
Norm-based capacity control in neural networks
B Neyshabur, R Tomioka, N Srebro
Conference on Learning Theory, 1376-1401, 2015
3462015
Learnability, stability and uniform convergence
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
The Journal of Machine Learning Research 11, 2635-2670, 2010
3372010
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
B Neyshabur, S Bhojanapalli, N Srebro
arXiv preprint arXiv:1707.09564, 2017
3292017
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
3202016
Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm
D Needell, R Ward, N Srebro
Advances in neural information processing systems 27, 1017-1025, 2014
3192014
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
3192008
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
3152014
Better mini-batch algorithms via accelerated gradient methods
A Cotter, O Shamir, N Srebro, K Sridharan
arXiv preprint arXiv:1106.4574, 2011
2982011
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
291*2018
Stochastic Convex Optimization.
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
COLT 2 (4), 5, 2009
2692009
Sustav trenutno ne može provesti ovu radnju. Pokušajte ponovo kasnije.
Članci 1–20