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20152025
most citedUn-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

67 citations · 176 across the 22 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

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…

cs.DS201724 cited

Efficient Convex Optimization with Membership Oracles

Yin Tat Lee, Aaron Sidford, Santosh S. Vempala

We consider the problem of minimizing a convex function over a convex set given access only to an evaluation oracle for the function and a membership oracle for the set. We give a…

math.OC2017

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

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

We develop and analyze a variant of Nesterov's accelerated gradient descent (AGD) for minimization of smooth non-convex functions. We prove that one of two cases occurs: either our…