From the 1 of 5 linked papers with an AI index.
5 papers
First-Order Methods for Distributionally Robust Constrained Optimization
Hubert Villuendas, Mathieu Besançon, Jérôme Malick
The paper introduces a stochastic algorithm that combines entropic regularization with a stochastic Frank‑Wolfe method to solve Wasserstein distributionally robust optimization pro…
: 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…
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…
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…
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…