65 citations · 74 across the 3 of their papers we have counts for
11 papers
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking
Andrew N Carr, Quentin Berthet, Mathieu Blondel +2
Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of modalities. In this work, we advance self-supervised l…
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu, Quentin Berthet, Francis Bach
Optimal transport is a foundational problem in optimization, that allows to compare probability distributions while taking into account geometric aspects. Its optimal objective val…
Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet +1
The sorting operation is one of the most commonly used building blocks in computer programming. In machine learning, it is often used for robust statistics. However, seen as a func…
Learning with Differentiable Perturbed Optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul +3
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete d…
Minimax estimation of smooth densities in Wasserstein distance
Jonathan Niles-Weed, Quentin Berthet
We study nonparametric density estimation problems where error is measured in the Wasserstein distance, a metric on probability distributions popular in many areas of statistics an…
Regularized Contextual Bandits
Xavier Fontaine, Quentin Berthet, Vianney Perchet
We consider the stochastic contextual bandit problem with additional regularization. The motivation comes from problems where the policy of the agent must be close to some baseline…