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Ohad Shamir
Ohad Shamir
Geverifieerd e-mailadres voor weizmann.ac.il - Homepage
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Geciteerd door
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The power of depth for feedforward neural networks
R Eldan, O Shamir
Conference on learning theory, 907-940, 2016
8292016
Learnability, stability and uniform convergence
S Shalev-Shwartz, O Shamir, N Srebro, K Sridharan
The Journal of Machine Learning Research 9999, 2635-2670, 2010
745*2010
Optimal Distributed Online Prediction Using Mini-Batches.
O Dekel, R Gilad-Bachrach, O Shamir, L Xiao
Journal of Machine Learning Research 13 (1), 2012
6972012
Making gradient descent optimal for strongly convex stochastic optimization
A Rakhlin, O Shamir, K Sridharan
arXiv preprint arXiv:1109.5647, 2011
6782011
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
O Shamir, T Zhang
International conference on machine learning, 71-79, 2013
5622013
Communication-efficient distributed optimization using an approximate newton-type method
O Shamir, N Srebro, T Zhang
International conference on machine learning, 1000-1008, 2014
5382014
On the computational efficiency of training neural networks
R Livni, S Shalev-Shwartz, O Shamir
Advances in neural information processing systems 27, 2014
5342014
Size-independent sample complexity of neural networks
N Golowich, A Rakhlin, O Shamir
Conference On Learning Theory, 297-299, 2018
4412018
Better mini-batch algorithms via accelerated gradient methods
A Cotter, O Shamir, N Srebro, K Sridharan
Advances in neural information processing systems 24, 2011
3502011
Adaptively learning the crowd kernel
O Tamuz, C Liu, S Belongie, O Shamir, AT Kalai
arXiv preprint arXiv:1105.1033, 2011
2982011
Nonstochastic multi-armed bandits with graph-structured feedback
N Alon, N Cesa-Bianchi, C Gentile, S Mannor, Y Mansour, O Shamir
SIAM Journal on Computing 46 (6), 1785-1826, 2017
258*2017
Spurious local minima are common in two-layer relu neural networks
I Safran, O Shamir
International conference on machine learning, 4433-4441, 2018
2512018
Learning and generalization with the information bottleneck
O Shamir, S Sabato, N Tishby
Theoretical Computer Science 411 (29-30), 2696-2711, 2010
2102010
Depth-width tradeoffs in approximating natural functions with neural networks
I Safran, O Shamir
International conference on machine learning, 2979-2987, 2017
203*2017
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback
O Shamir
The Journal of Machine Learning Research 18 (1), 1703-1713, 2017
1952017
Communication complexity of distributed convex learning and optimization
Y Arjevani, O Shamir
Advances in neural information processing systems 28, 2015
1942015
Learning to classify with missing and corrupted features
O Dekel, O Shamir
Proceedings of the 25th international conference on Machine learning, 216-223, 2008
1942008
On the complexity of bandit and derivative-free stochastic convex optimization
O Shamir
Conference on Learning Theory, 3-24, 2013
1872013
Proving the lottery ticket hypothesis: Pruning is all you need
E Malach, G Yehudai, S Shalev-Schwartz, O Shamir
International Conference on Machine Learning, 6682-6691, 2020
1862020
Failures of gradient-based deep learning
S Shalev-Shwartz, O Shamir, S Shammah
International Conference on Machine Learning, 3067-3075, 2017
1752017
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Artikelen 1–20