Fabian Pedregosa

research

∙ 02/24/2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes

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

Robert M. Gower, et al. ∙

research

∙ 01/13/2022

GradMax: Growing Neural Networks using Gradient Information

The architecture and the parameters of neural networks are often optimiz...

Utku Evci, et al. ∙

research

∙ 05/31/2021

Efficient and Modular Implicit Differentiation

Automatic differentiation (autodiff) has revolutionized machine learning...

Mathieu Blondel, et al. ∙

research

∙ 05/19/2021

Boosting Variational Inference With Locally Adaptive Step-Sizes

Variational Inference makes a trade-off between the capacity of the vari...

Gideon Dresdner, et al. ∙

research

∙ 02/08/2021

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality

We propose a new framework, inspired by random matrix theory, for analyz...

Courtney Paquette, et al. ∙

research

∙ 10/05/2020

Average-case Acceleration for Bilinear Games and Normal Matrices

Advances in generative modeling and adversarial learning have given rise...

Carles Domingo Enrich, et al. ∙

research

∙ 06/08/2020

Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis

Average-case analysis computes the complexity of an algorithm averaged o...

Courtney Paquette, et al. ∙

research

∙ 02/12/2020

Average-case Acceleration Through Spectral Density Estimation

We develop a framework for designing optimal quadratic optimization meth...

Fabian Pedregosa, et al. ∙

research

∙ 10/08/2019

A Test for Shared Patterns in Cross-modal Brain Activation Analysis

Determining the extent to which different cognitive modalities (understo...

Elena Kalinina, et al. ∙

research

∙ 07/23/2019

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

SciPy is an open source scientific computing library for the Python prog...

Pauli Virtanen, et al. ∙

research

∙ 06/25/2019

The Difficulty of Training Sparse Neural Networks

We investigate the difficulties of training sparse neural networks and m...

Utku Evci, et al. ∙

research

∙ 06/18/2019

Information matrices and generalization

This work revisits the use of information criteria to characterize the g...

Valentin Thomas, et al. ∙

research

∙ 04/09/2018

Frank-Wolfe Splitting via Augmented Lagrangian Method

Minimizing a function over an intersection of convex sets is an importan...

Gauthier Gidel, et al. ∙

research

∙ 04/06/2018

Adaptive Three Operator Splitting

We propose and analyze a novel adaptive step size variant of the Davis-Y...

Fabian Pedregosa, et al. ∙

research

∙ 03/20/2018

Frank-Wolfe with Subsampling Oracle

We analyze two novel randomized variants of the Frank-Wolfe (FW) or cond...

Thomas Kerdreux, et al. ∙

research

∙ 07/20/2017

Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization

Due to their simplicity and excellent performance, parallel asynchronous...

Fabian Pedregosa, et al. ∙

research

∙ 10/25/2016

On the convergence rate of the three operator splitting scheme

The three operator splitting scheme was recently proposed by [Davis and ...

Fabian Pedregosa, et al. ∙

research

∙ 06/15/2016

ASAGA: Asynchronous Parallel SAGA

We describe ASAGA, an asynchronous parallel version of the incremental g...

Rémi Leblond, et al. ∙

research

∙ 02/07/2016

Hyperparameter optimization with approximate gradient

Most models in machine learning contain at least one hyperparameter to c...

Fabian Pedregosa, et al. ∙

research

∙ 12/12/2014

Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimag...

Alexandre Abraham, et al. ∙

research

∙ 08/10/2013

Second order scattering descriptors predict fMRI activity due to visual textures

Second layer scattering descriptors are known to provide good classifica...

Michael Eickenberg, et al. ∙

research

∙ 07/16/2012

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the se...

Fabian Pedregosa, et al. ∙

research

∙ 07/15/2012

Improved brain pattern recovery through ranking approaches

Inferring the functional specificity of brain regions from functional Ma...

Fabian Pedregosa, et al. ∙

Fabian Pedregosa

Featured Co-authors

Yoshua Bengio

Simon Lacoste-Julien

Alexandre Gramfort

Marco Cuturi

Gaël Varoquaux

Bertrand Thirion

Gunnar Rätsch

Francesco Locatello

Jean-Philippe Vert

Gauthier Gidel

Mathieu Blondel

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes

GradMax: Growing Neural Networks using Gradient Information

Efficient and Modular Implicit Differentiation

Boosting Variational Inference With Locally Adaptive Step-Sizes

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality

Average-case Acceleration for Bilinear Games and Normal Matrices

Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis

Average-case Acceleration Through Spectral Density Estimation

A Test for Shared Patterns in Cross-modal Brain Activation Analysis

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

The Difficulty of Training Sparse Neural Networks

Information matrices and generalization

Frank-Wolfe Splitting via Augmented Lagrangian Method

Adaptive Three Operator Splitting

Frank-Wolfe with Subsampling Oracle

Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization

On the convergence rate of the three operator splitting scheme

ASAGA: Asynchronous Parallel SAGA

Hyperparameter optimization with approximate gradient

Machine Learning for Neuroimaging with Scikit-Learn

Second order scattering descriptors predict fMRI activity due to visual textures

Learning to rank from medical imaging data

Improved brain pattern recovery through ranking approaches