activity
20152017
most citedUn-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

67 citations · 97 across the 5 of their papers we have counts for

collaborators

13 papers

cs.DS20191 cited

A Direct Iteration Parallel Algorithm for Optimal Transport

Arun Jambulapati, Aaron Sidford, Kevin Tian

Optimal transportation, or computing the Wasserstein or ``earth mover's'' distance between two distributions, is a fundamental primitive which arises in many learning and statistic…

cs.DS20191 cited

Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation

Moses Charikar, Kirankumar Shiragur, Aaron Sidford

Estimating symmetric properties of a distribution, e.g. support size, coverage, entropy, distance to uniformity, are among the most fundamental problems in algorithmic statistics.…

stat.ML20176 cited

Leverage Score Sampling for Faster Accelerated Regression and ERM

Naman Agarwal, Sham Kakade, Rahul Kidambi +3

Given a matrix and a vector , we show how to compute an -approximate solution to the regression problem $ \min_{x\in\m…

math.OC2017

Lower Bounds for Finding Stationary Points II: First-Order Methods

Yair Carmon, John C. Duchi, Oliver Hinder +1

We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex high-dimensional functions using first-order methods. We prove that deterministic…

cs.DS2017

Efficient Spectral Sketches for the Laplacian and its Pseudoinverse

Arun Jambulapati, Aaron Sidford

In this paper we consider the problem of efficiently computing -sketches for the Laplacian and its pseudoinverse. Given a Laplacian and an error tolerance , we seek to constr…

cs.CC2017

Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space

Jack Murtagh, Omer Reingold, Aaron Sidford +1

We give a deterministic -space algorithm for approximately solving linear systems given by Laplacians of undirected graphs, and consequently also approximating h…