261 citations · 576 across the 22 of their papers we have counts for
9 papers · 1 filter
Differentiable Clustering with Perturbed Spanning Forests
Lawrence Stewart, Francis S Bach, Felipe Llinares López +1
We introduce a differentiable clustering method based on stochastic perturbations of minimum-weight spanning forests. This allows us to include clustering in end-to-end trainable p…
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
Marin Ballu, Quentin Berthet
Optimal transport is an important tool in machine learning, allowing to capture geometric properties of the data through a linear program on transport polytopes. We present a singl…
Regression as Classification: Influence of Task Formulation on Neural Network Features
Lawrence Stewart, Francis Bach, Quentin Berthet +1
Neural networks can be trained to solve regression problems by using gradient-based methods to minimize the square loss. However, practitioners often prefer to reformulate regressi…
Efficient and Modular Implicit Differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi +5
Automatic differentiation (autodiff) has revolutionized machine learning. It allows to express complex computations by composing elementary ones in creative ways and removes the bu…
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…
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…
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Edouard Grave, Armand Joulin, Quentin Berthet
We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recentl…
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Quentin Berthet, Vianney Perchet
We consider the problem of bandit optimization, inspired by stochastic optimization and online learning problems with bandit feedback. In this problem, the objective is to minimize…
Average-case Hardness of RIP Certification
Tengyao Wang, Quentin Berthet, Yaniv Plan
The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models. It is of high interest in compressed sensing and statistic…