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Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss


May 04, 2021
Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford


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Large-Scale Methods for Distributionally Robust Optimization


Oct 12, 2020
Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

* 59 pages, NeurIPS 2020 

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Coordinate Methods for Matrix Games


Sep 17, 2020
Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian

* Accepted at FOCS 2020 

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Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations


Jun 24, 2020
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

* Accepted to CONFERENCE ON LEARNING THEORY (COLT) 2020 

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Lower Bounds for Non-Convex Stochastic Optimization


Dec 05, 2019
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake Woodworth


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Variance Reduction for Matrix Games


Jul 03, 2019
Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian


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Unlabeled Data Improves Adversarial Robustness


Jun 10, 2019
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi


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A Rank-1 Sketch for Matrix Multiplicative Weights


Mar 07, 2019
Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian


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No bad local minima: Data independent training error guarantees for multilayer neural networks


May 30, 2016
Daniel Soudry, Yair Carmon


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