Dan Garber

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Elad HazanProfessor at Princeton University and Director Google AI PrincetonDirección de correo verificada de princeton.edu
Nathan SrebroProfessor, TTIC and University of ChicagoDirección de correo verificada de ttic.edu
Weiran WangUniversity of IowaDirección de correo verificada de uiowa.edu
Jialei WangUniversity of ChicagoDirección de correo verificada de uchicago.edu
Aaron SidfordStanford UniversityDirección de correo verificada de stanford.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Praneeth NetrapalliGoogle ResearchDirección de correo verificada de google.com
Chi JinAssistant Professor, Princeton UniversityDirección de correo verificada de princeton.edu
Shoham SabachAssociate Professor, Technion, Faculty of Data and Decision SciencesDirección de correo verificada de technion.ac.il
Edo Libertypinecone.ioDirección de correo verificada de edoliberty.com
Christos BoutsidisDirección de correo verificada de gs.com
Zohar KarninamazonDirección de correo verificada de amazon.com
Ofer MeshiResearch Scientist at GoogleDirección de correo verificada de google.com
Ohad ShamirWeizmann Institute of ScienceDirección de correo verificada de weizmann.ac.il
Tengyu MAStanford UniversityDirección de correo verificada de stanford.edu
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu

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Dan Garber

Associate Professor at Technion - Israel Institute of Technology

Dirección de correo verificada de technion.ac.il - Página principal

Machine Learning Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Faster rates for the Frank-Wolfe method over strongly-convex sets D Garber, E Hazan International Conference on Machine Learning, 541-549, 2015	221	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	173	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	105*	2016
Fast and simple PCA via convex optimization D Garber, E Hazan arXiv preprint arXiv:1509.05647, 2015	104	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	86	2014
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	58	2016
Communication-efficient algorithms for distributed stochastic principal component analysis D Garber, O Shamir, N Srebro International Conference on Machine Learning, 1203-1212, 2017	55	2017
Online learning of eigenvectors D Garber, E Hazan, T Ma International Conference on Machine Learning, 560-568, 2015	54	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	45	2016
Faster Projection-free Convex Optimization over the Spectrahedron D Garber arxiv, 2016	39	2016
Sublinear time algorithms for approximate semidefinite programming D Garber, E Hazan Mathematical Programming 158, 329-361, 2016	34	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	32	2019
Improved regret bounds for projection-free bandit convex optimization D Garber, B Kretzu International Conference on Artificial Intelligence and Statistics, 2196-2206, 2020	30	2020
Efficient online linear optimization with approximation algorithms D Garber Advances in Neural Information Processing Systems 30, 2017	30	2017
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	29*	2021
Revisiting projection-free online learning: the strongly convex case B Kretzu, D Garber International Conference on Artificial Intelligence and Statistics, 3592-3600, 2021	28	2021
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity D Garber arXiv preprint arXiv:2006.00558, 2020	26	2020
New projection-free algorithms for online convex optimization with adaptive regret guarantees D Garber, B Kretzu Conference on Learning Theory, 2326-2359, 2022	24	2022

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