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Journal Articles
- 2021
[j1]Dan Garber, Atara Kaplan, Shoham Sabach
:
Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems. Math. Program. 186(1): 185-208 (2021)
Conference and Workshop Papers
- 2021
- [c2]Atara Kaplan, Dan Garber:
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. NeurIPS 2021: 26332-26344 - 2019
- [c1]Dan Garber, Atara Kaplan:
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. AISTATS 2019: 286-294
Informal and Other Publications
- 2024
- [i7]Dan Garber, Atara Kaplan:
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization. CoRR abs/2402.09081 (2024) - 2023
- [i6]Dan Garber, Atara Kaplan:
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity. CoRR abs/2308.01677 (2023) - 2022
- [i5]Dan Garber, Atara Kaplan:
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR abs/2202.04026 (2022) - [i4]Dan Garber, Atara Kaplan:
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems. CoRR abs/2206.11523 (2022) - 2020
- [i3]Dan Garber, Atara Kaplan:
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization. CoRR abs/2012.10469 (2020) - 2018
- [i2]Dan Garber, Shoham Sabach, Atara Kaplan:
Fast Generalized Conditional Gradient Method with Applications to Matrix Recovery Problems. CoRR abs/1802.05581 (2018) - [i1]Dan Garber, Atara Kaplan:
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems. CoRR abs/1809.10477 (2018)
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