Dan Garber
Dan Garber
Verifierad e-postadress på technion.ac.il - Startsida
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Faster rates for the frank-wolfe method over strongly-convex sets
D Garber, E Hazan
32nd International Conference on Machine Learning, ICML 2015, 2015
1152015
A linearly convergent variant of the conditional gradient algorithm under strong convexity, with applications to online and stochastic optimization
D Garber, E Hazan
SIAM Journal on Optimization 26 (3), 1493-1528, 2016
91*2016
Fast and simple PCA via convex optimization
D Garber, E Hazan
arXiv preprint arXiv:1509.05647, 2015
762015
Faster eigenvector computation via shift-and-invert preconditioning
D Garber, E Hazan, C Jin, C Musco, P Netrapalli, A Sidford
International Conference on Machine Learning, 2626-2634, 2016
68*2016
Online principal components analysis
C Boutsidis, D Garber, Z Karnin, E Liberty
Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014
632014
Online Learning of Eigenvectors.
D Garber, E Hazan, T Ma
ICML, 560-568, 2015
412015
Approximating semidefinite programs in sublinear time
D Garber, E Hazan
Advances in Neural Information Processing Systems, 1080-1088, 2011
342011
Playing non-linear games with linear oracles
D Garber, E Hazan
2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 420-428, 2013
282013
Linear-memory and decomposition-invariant linearly convergent conditional gradient algorithm for structured polytopes
D Garber, O Meshi
Advances in neural information processing systems, 1001-1009, 2016
252016
Communication-efficient algorithms for distributed stochastic principal component analysis
D Garber, O Shamir, N Srebro
arXiv preprint arXiv:1702.08169, 2017
202017
Efficient globally convergent stochastic optimization for canonical correlation analysis
W Wang, J Wang, D Garber, N Srebro
Advances in Neural Information Processing Systems, 766-774, 2016
192016
Faster Projection-free Convex Optimization over the Spectrahedron
D Garber
arxiv, 2016
192016
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems
D Garber, A Kaplan, S Sabach
Mathematical Programming, 1-24, 2019
13*2019
Efficient coordinate-wise leading eigenvector computation
J Wang, W Wang, D Garber, N Srebro
Algorithmic Learning Theory, 806-820, 2018
122018
Sublinear time algorithms for approximate semidefinite programming
D Garber, E Hazan
Mathematical Programming 158 (1-2), 329-361, 2016
122016
Stochastic Canonical Correlation Analysis.
C Gao, D Garber, N Srebro, J Wang, W Wang
Journal of Machine Learning Research 20 (167), 1-46, 2019
102019
Efficient online linear optimization with approximation algorithms
D Garber
Advances in Neural Information Processing Systems, 627-635, 2017
62017
Logarithmic regret for online gradient descent beyond strong convexity
D Garber
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
5*2019
Fast stochastic algorithms for low-rank and nonsmooth matrix problems
D Garber, A Kaplan
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
42019
Learning of optimal forecast aggregation in partial evidence environments
Y Babichenko, D Garber
arXiv preprint arXiv:1802.07107, 2018
32018
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Artiklar 1–20