Lenaïc Chizat | DeepAI

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∙ 07/25/2023

Computational Guarantees for Doubly Entropic Wasserstein Barycenters via Damped Sinkhorn Iterations

We study the computation of doubly regularized Wasserstein barycenters, ...

0 Lenaïc Chizat, et al. ∙

∙ 05/26/2023

Local Convergence of Gradient Methods for Min-Max Games under Partial Curvature

We study the convergence to local Nash equilibria of gradient methods fo...

0 Guillaume Wang, et al. ∙

∙ 03/31/2023

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks

In supervised learning, the regularization path is sometimes used as a c...

0 Sebastian Neumayer, et al. ∙

∙ 03/21/2023

Doubly Regularized Entropic Wasserstein Barycenters

We study a general formulation of regularized Wasserstein barycenters th...

0 Lenaïc Chizat, et al. ∙

∙ 11/29/2022

Infinite-width limit of deep linear neural networks

This paper studies the infinite-width limit of deep linear neural networ...

0 Lenaïc Chizat, et al. ∙

∙ 11/16/2022

On the symmetries in the dynamics of wide two-layer neural networks

We consider the idealized setting of gradient flow on the population ris...

0 Karl Hajjar, et al. ∙

∙ 11/02/2022

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

We consider the problem of computing mixed Nash equilibria of two-player...

0 Guillaume Wang, et al. ∙

∙ 05/14/2022

Trajectory Inference via Mean-field Langevin in Path Space

Trajectory inference aims at recovering the dynamics of a population fro...

0 Lenaïc Chizat, et al. ∙

∙ 10/29/2021

Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit

To theoretically understand the behavior of trained deep neural networks...

0 Karl Hajjar, et al. ∙

∙ 10/15/2021

Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization

Many supervised machine learning methods are naturally cast as optimizat...

0 Francis Bach, et al. ∙

∙ 06/15/2020

Faster Wasserstein Distance Estimation with the Sinkhorn Divergence

The squared Wasserstein distance is a natural quantity to compare probab...

0 Lenaïc Chizat, et al. ∙

∙ 03/12/2020

Statistical and Topological Properties of Sliced Probability Divergences

The idea of slicing divergences has been proven to be successful when co...

0 Kimia Nadjahi, et al. ∙

∙ 02/11/2020

Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

Neural networks trained to minimize the logistic (a.k.a. cross-entropy) ...

2 Lenaïc Chizat, et al. ∙

∙ 07/24/2019

Sparse Optimization on Measures with Over-parameterized Gradient Descent

Minimizing a convex function of a measure with a sparsity-inducing penal...

0 Lenaïc Chizat, et al. ∙

∙ 03/16/2019

HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models

With huge data acquisition progresses realized in the past decades and a...

1 Jean-Luc Peyrot, et al. ∙

∙ 12/19/2018

A Note on Lazy Training in Supervised Differentiable Programming

In a series of recent theoretical works, it has been shown that strongly...

0 Lenaïc Chizat, et al. ∙

∙ 10/05/2018

Sample Complexity of Sinkhorn divergences

Optimal transport (OT) and maximum mean discrepancies (MMD) are now rout...

0 Aude Genevay, et al. ∙

∙ 05/24/2018

On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Many tasks in machine learning and signal processing can be solved by mi...

8 Lenaïc Chizat, et al. ∙

∙ 12/20/2016

Quantum Optimal Transport for Tensor Field Processing

This article introduces a new notion of optimal transport (OT) between t...

0 Gabriel Peyré, et al. ∙

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