Robert M. Gower | DeepAI

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∙ 07/26/2023

Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM

Here we develop variants of SGD (stochastic gradient descent) with an ad...

0 Guillaume Garrigos, et al. ∙

∙ 05/27/2023

A Model-Based Method for Minimizing CVaR and Beyond

We develop a variant of the stochastic prox-linear method for minimizing...

0 Si Yi Meng, et al. ∙

∙ 01/12/2023

A Stochastic Proximal Polyak Step Size

Recently, the stochastic Polyak step size (SPS) has emerged as a competi...

0 Fabian Schaipp, et al. ∙

∙ 10/04/2022

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies

We consider infinite-horizon discounted Markov decision processes and st...

0 Rui Yuan, et al. ∙

∙ 07/17/2022

SP2: A Second Order Stochastic Polyak Method

Recently the "SP" (Stochastic Polyak step size) method has emerged as a ...

5 Shuang Li, et al. ∙

∙ 02/24/2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes

Tuning the step size of stochastic gradient descent is tedious and error...

2 Robert M. Gower, et al. ∙

∙ 07/23/2021

A general sample complexity analysis of vanilla policy gradient

The policy gradient (PG) is one of the most popular methods for solving ...

0 Rui Yuan, et al. ∙

∙ 06/22/2021

Stochastic Polyak Stepsize with a Moving Target

We propose a new stochastic gradient method that uses recorded past loss...

1 Robert M. Gower, et al. ∙

∙ 06/19/2021

SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums

We present a principled approach for designing stochastic Newton methods...

0 Jiabin Chen, et al. ∙

∙ 10/02/2020

Variance-Reduced Methods for Machine Learning

Stochastic optimization lies at the heart of machine learning, and its c...

20 Robert M. Gower, et al. ∙

∙ 07/29/2020

A new framework for the computation of Hessians

We investigate the computation of Hessian matrices via Automatic Differe...

0 Robert M. Gower, et al. ∙

∙ 06/22/2020

Sketched Newton-Raphson

We propose a new globally convergent stochastic second order method. Our...

0 Rui Yuan, et al. ∙

∙ 06/20/2020

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

We present a unified theorem for the convergence analysis of stochastic ...

30 Ahmed Khaled, et al. ∙

∙ 06/18/2020

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

We provide several convergence theorems for SGD for two large classes of...

17 Robert M. Gower, et al. ∙

∙ 06/14/2020

On the convergence of the Stochastic Heavy Ball Method

We provide a comprehensive analysis of the Stochastic Heavy Ball (SHB) m...

1 Othmane Sebbouh, et al. ∙

∙ 06/01/2020

Factorial Powers for Stochastic Optimization

The convergence rates for convex and non-convex optimization methods dep...

17 Aaron Defazio, et al. ∙

∙ 02/26/2020

Fast Linear Convergence of Randomized BFGS

Since the late 1950's when quasi-Newton methods first appeared, they hav...

0 Dmitry Kovalev, et al. ∙

∙ 07/31/2019

Towards closing the gap between the theory and practice of SVRG

Among the very first variance reduced stochastic methods for solving the...

0 Othmane Sebbouh, et al. ∙

∙ 01/31/2019

Optimal mini-batch and step sizes for SAGA

Recently it has been shown that the step sizes of a family of variance r...

22 Nidham Gazagnadou, et al. ∙

∙ 03/04/2018

Greedy stochastic algorithms for entropy-regularized optimal transport problems

Optimal transport (OT) distances are finding evermore applications in ma...

0 Brahim Khalil Abid, et al. ∙

∙ 02/12/2018

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...

1 Robert M. Gower, et al. ∙

∙ 10/20/2017

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...

0 Robert M. Gower, et al. ∙

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