Robert M. Gower

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Variance-Reduced Methods for Machine Learning

Stochastic optimization lies at the heart of machine learning, and its c...
10/02/2020 ∙ by Robert M. Gower, et al. ∙ 20 ∙ share

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A new framework for the computation of Hessians

We investigate the computation of Hessian matrices via Automatic Differe...
07/29/2020 ∙ by Robert M. Gower, et al. ∙ 0 ∙ share

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Sketched Newton-Raphson

We propose a new globally convergent stochastic second order method. Our...
06/22/2020 ∙ by Rui Yuan, et al. ∙ 0 ∙ share

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Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

We present a unified theorem for the convergence analysis of stochastic ...
06/20/2020 ∙ by Ahmed Khaled, et al. ∙ 30 ∙ share

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SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

We provide several convergence theorems for SGD for two large classes of...
06/18/2020 ∙ by Robert M. Gower, et al. ∙ 17 ∙ share

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On the convergence of the Stochastic Heavy Ball Method

We provide a comprehensive analysis of the Stochastic Heavy Ball (SHB) m...
06/14/2020 ∙ by Othmane Sebbouh, et al. ∙ 0 ∙ share

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Factorial Powers for Stochastic Optimization

The convergence rates for convex and non-convex optimization methods dep...
06/01/2020 ∙ by Aaron Defazio, et al. ∙ 16 ∙ share

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Fast Linear Convergence of Randomized BFGS

Since the late 1950's when quasi-Newton methods first appeared, they hav...
02/26/2020 ∙ by Dmitry Kovalev, et al. ∙ 0 ∙ share

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Towards closing the gap between the theory and practice of SVRG

Among the very first variance reduced stochastic methods for solving the...
07/31/2019 ∙ by Othmane Sebbouh, et al. ∙ 0 ∙ share

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Optimal mini-batch and step sizes for SAGA

Recently it has been shown that the step sizes of a family of variance r...
01/31/2019 ∙ by Nidham Gazagnadou, et al. ∙ 22 ∙ share

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Greedy stochastic algorithms for entropy-regularized optimal transport problems

Optimal transport (OT) distances are finding evermore applications in ma...
03/04/2018 ∙ by Brahim Khalil Abid, et al. ∙ 0 ∙ share

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Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

We present the first accelerated randomized algorithm for solving linear...
02/12/2018 ∙ by Robert M. Gower, et al. ∙ 1 ∙ share

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Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods

Our goal is to improve variance reducing stochastic methods through bett...
10/20/2017 ∙ by Robert M. Gower, et al. ∙ 0 ∙ share

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Robert M. Gower

Featured Co-authors

Francis Bach

Peter Richtarik

Alessandro Lazaric

Mark Schmidt

Joseph Salmon

Aaron Defazio

Nicolas Le Roux

Filip Hanzely

Nicolas Loizou

Dmitry Kovalev

Samy Jelassi

Variance-Reduced Methods for Machine Learning

A new framework for the computation of Hessians

Sketched Newton-Raphson

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

On the convergence of the Stochastic Heavy Ball Method

Factorial Powers for Stochastic Optimization

Fast Linear Convergence of Randomized BFGS

Towards closing the gap between the theory and practice of SVRG

Optimal mini-batch and step sizes for SAGA

Greedy stochastic algorithms for entropy-regularized optimal transport problems

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods