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

4 May 2021

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

11 / 11 papers shown

Title
Lower Bounds for Non-Convex Stochastic Optimization Yossi Arjevani Y. Carmon John C. Duchi Dylan J. Foster Nathan Srebro Blake E. Woodworth 65 349 0 05 Dec 2019
Variance Reduction for Matrix Games Y. Carmon Yujia Jin Aaron Sidford Kevin Tian 36 65 0 03 Jul 2019
Complexity of Highly Parallel Non-Smooth Convex Optimization Sébastien Bubeck Qijia Jiang Y. Lee Yuanzhi Li Aaron Sidford 42 55 0 25 Jun 2019
Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity Chaobing Song Yong Jiang Yi Ma 265 19 0 03 Jun 2019
Lower Bounds for Parallel and Randomized Convex Optimization Jelena Diakonikolas Cristóbal Guzmán 50 44 0 05 Nov 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 79 572 0 04 Jul 2018
Tight Complexity Bounds for Optimizing Composite Objectives Blake E. Woodworth Nathan Srebro 109 185 0 25 May 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 85 580 0 18 Mar 2016
Minimizing the Maximal Loss: How and Why? Shai Shalev-Shwartz Y. Wexler 26 81 0 04 Feb 2016
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization Roy Frostig Rong Ge Sham Kakade Aaron Sidford 36 150 0 24 Jun 2015
Sublinear Optimization for Machine Learning K. Clarkson Elad Hazan David P. Woodruff 55 138 0 21 Oct 2010