Fabian Pedregosa

research

∙ 12/28/2022

On Implicit Bias in Overparameterized Bilevel Optimization

Many problems in machine learning involve bilevel optimization (BLO), in...

0 Paul Vicol, et al. ∙

research

∙ 12/08/2022

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces

Many machine learning problems encode their data as a matrix with a poss...

0 Charline Le Lan, et al. ∙

research

∙ 11/09/2022

Extragradient with Positive Momentum is Optimal for Games with Cross-Shaped Jacobian Spectrum

The extragradient method has recently gained increasing attention, due t...

0 Junhyung Lyle Kim, et al. ∙

research

∙ 10/10/2022

Second-order regression models exhibit progressive sharpening to the edge of stability

Recent studies of gradient descent with large step sizes have shown that...

0 Atish Agarwala, et al. ∙

research

∙ 09/27/2022

The Curse of Unrolling: Rate of Differentiating Through Optimization

Computing the Jacobian of the solution of an optimization problem is a c...

0 Damien Scieur, et al. ∙

research

∙ 06/20/2022

Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime

The recently developed average-case analysis of optimization methods all...

0 Leonardo Cunha, et al. ∙

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...

2 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...

0 Utku Evci, et al. ∙

research

∙ 05/31/2021

Efficient and Modular Implicit Differentiation

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

0 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...

0 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...

0 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...

0 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...

8 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...

0 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...

8 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...

0 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...

5 Utku Evci, et al. ∙

research

∙ 06/18/2019

Information matrices and generalization

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

4 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...

0 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...

0 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...

0 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...

0 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 ...

0 Fabian Pedregosa, et al. ∙

research

∙ 06/15/2016

ASAGA: Asynchronous Parallel SAGA

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

0 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...

0 Fabian Pedregosa, et al. ∙

research

∙ 12/12/2014

Machine Learning for Neuroimaging with Scikit-Learn

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

0 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...

0 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...

0 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...

0 Fabian Pedregosa, et al. ∙

Fabian Pedregosa

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