| Faster rates for the Frank-Wolfe method over strongly-convex sets D Garber, E Hazan International Conference on Machine Learning, 541-549, 2015 | 225 | 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 |
Elad HazanProfessor at Princeton University and Director Google AI PrincetonE-mail confirmado em princeton.edu
