69 citations · 316 across the 30 of their papers we have counts for
4 papers · 1 filter
Optimal Complexity and Certification of Bregman First-Order Methods
Radu-Alexandru Dragomir, Adrien Taylor, Alexandre d'Aspremont +1
We provide a lower bound showing that the convergence rate of the NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions satisfying the -smoothne…
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning
Jérôme Bolte, Edouard Pauwels
Modern problems in AI or in numerical analysis require nonsmooth approaches with a flexible calculus. We introduce generalized derivatives called conservative fields for which we d…
An Inertial Newton Algorithm for Deep Learning
Camille Castera, Jérôme Bolte, Cédric Févotte +1
We introduce a new second-order inertial optimization method for machine learning called INNA. It exploits the geometry of the loss function while only requiring stochastic approxi…
Quartic First-Order Methods for Low-Rank Minimization
Radu-Alexandru Dragomir, Alexandre d'Aspremont, Jérôme Bolte
We study a generalized nonconvex Burer-Monteiro formulation for low-rank minimization problems. We use recent results on non-Euclidean first order methods to provide efficient and…