Dan Garber

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Elad HazanProfessor at Princeton University and Director Google AI PrincetonEmail yang diverifikasi di princeton.edu
Nathan SrebroProfessor, TTIC and University of ChicagoEmail yang diverifikasi di ttic.edu
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Praneeth NetrapalliGoogle ResearchEmail yang diverifikasi di google.com
Chi JinAssistant Professor, Princeton UniversityEmail yang diverifikasi di princeton.edu
Shoham SabachAssociate Professor, Technion, Faculty of Data and Decision SciencesEmail yang diverifikasi di technion.ac.il
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Dan Garber

Associate Professor at Technion - Israel Institute of Technology

Email yang diverifikasi di technion.ac.il - Beranda

Machine Learning Optimization


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Faster rates for the Frank-Wolfe method over strongly-convex sets D Garber, E Hazan International Conference on Machine Learning, 541-549, 2015	226	2015
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	178	2016
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	111*	2016
Fast and simple PCA via convex optimization D Garber, E Hazan arXiv preprint arXiv:1509.05647, 2015	107	2015
Online principal components analysis C Boutsidis, D Garber, Z Karnin, E Liberty Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014	89	2014
Communication-efficient algorithms for distributed stochastic principal component analysis D Garber, O Shamir, N Srebro International Conference on Machine Learning, 1203-1212, 2017	61	2017
Linear-memory and decomposition-invariant linearly convergent conditional gradient algorithm for structured polytopes D Garber, O Meshi Advances in neural information processing systems 29, 2016	60	2016
Online learning of eigenvectors D Garber, E Hazan, T Ma International Conference on Machine Learning, 560-568, 2015	56	2015
Playing non-linear games with linear oracles D Garber, E Hazan 2013 IEEE 54th annual symposium on foundations of computer science, 420-428, 2013	47	2013
Approximating semidefinite programs in sublinear time D Garber, E Hazan Advances in Neural Information Processing Systems 24, 2011	47	2011
Efficient globally convergent stochastic optimization for canonical correlation analysis W Wang, J Wang, D Garber, N Srebro Advances in Neural Information Processing Systems 29, 2016	46	2016
Faster Projection-free Convex Optimization over the Spectrahedron D Garber arxiv, 2016	40	2016
Sublinear time algorithms for approximate semidefinite programming D Garber, E Hazan Mathematical Programming 158, 329-361, 2016	36	2016
Stochastic canonical correlation analysis C Gao, D Garber, N Srebro, J Wang, W Wang Journal of Machine Learning Research 20 (167), 1-46, 2019	34	2019
Improved regret bounds for projection-free bandit convex optimization D Garber, B Kretzu International Conference on Artificial Intelligence and Statistics, 2196-2206, 2020	32	2020
Efficient online linear optimization with approximation algorithms D Garber Advances in Neural Information Processing Systems 30, 2017	32	2017
New projection-free algorithms for online convex optimization with adaptive regret guarantees D Garber, B Kretzu Conference on Learning Theory, 2326-2359, 2022	30	2022
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems D Garber, A Kaplan, S Sabach Mathematical Programming 186 (1), 185-208, 2021	30*	2021
Revisiting projection-free online learning: the strongly convex case B Kretzu, D Garber International Conference on Artificial Intelligence and Statistics, 3592-3600, 2021	29	2021
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity D Garber arXiv preprint arXiv:2006.00558, 2020	28	2020

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