most citedSharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods

5 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2022

On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood

Moses Charikar, Zhihao Jiang, Kirankumar Shiragur +1

We provide an efficient unified plug-in approach for estimating symmetric properties of distributions given independent samples. Our estimator is based on profile-maximum-likel…

cs.DS2022

Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification

Arun Jambulapati, Yang P. Liu, Aaron Sidford

We present an algorithm that given any -vertex, -edge, rank hypergraph constructs a spectral sparsifier with hyperedges in nearly-li…

cs.DS20221 cited

Semi-Random Sparse Recovery in Nearly-Linear Time

Jonathan A. Kelner, Jerry Li, Allen Liu +2

Sparse recovery is one of the most fundamental and well-studied inverse problems. Standard statistical formulations of the problem are provably solved by general convex programming…

math.OC20225 cited

Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods

Yujia Jin, Aaron Sidford, Kevin Tian

We design accelerated algorithms with improved rates for several fundamental classes of optimization problems. Our algorithms all build upon techniques related to the analysis of p…

math.OC2021

Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales

Jonathan Kelner, Annie Marsden, Vatsal Sharan +3

We provide new gradient-based methods for efficiently solving a broad class of ill-conditioned optimization problems. We consider the problem of minimizing a function $f : \mathbb{…