From the 1 of 5 linked papers with an AI index.
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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…
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…
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…