7 papers
Low-Budget Active Learning through Entropic Optimal Transport
Rim Hajal, Mathieu Besançon, Jérôme Malick
We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high accuracy on the selection only…
First-Order Methods for Wasserstein Distributionally Robust Constrained Optimization
Hubert Villuendas, Mathieu Besançon, Jérôme Malick
We consider constrained problems in which input data are affected by errors. In such settings, Wasserstein distributionally robust optimization provides a principled framework to m…
Knapsack with compactness: a semidefinite approach
Hubert Villuendas, Mathieu Besançon, Jérôme Malick
The min-knapsack problem with compactness constraints extends the classical knapsack problem, in the case of ordered items, by introducing a restriction ensuring that they cannot b…
The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we examine the time it takes for stochastic gradient descent (SGD) to reach the global minimum of a general, non-convex loss function. We approach this question thro…
: a library for Wasserstein distributionally robust machine learning
Florian Vincent, Waïss Azizian, Franck Iutzeler +1
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using Wasserstein distances, popular i…
Delay-tolerant distributed Bregman proximal algorithms
S. Chraibi, F. Iutzeler, J. Malick +1
Many problems in machine learning write as the minimization of a sum of individual loss functions over the training examples. These functions are usually differentiable but, in som…
Universal generalization guarantees for Wasserstein distributionally robust models
Tam Le, Jérôme Malick
Distributionally robust optimization has emerged as an attractive way to train robust machine learning models, capturing data uncertainty and distribution shifts. Recent statistica…