51 citations · 202 across the 34 of their papers we have counts for
4 papers · 1 filter
Rate of convergence for geometric inference based on the empirical Christoffel function
Mai Trang Vu, François Bachoc, Edouard Pauwels
We consider the problem of estimating the support of a measure from a finite, independent, sample. The estimators which are considered are constructed based on the empirical Christ…
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…
Semi-algebraic approximation using Christoffel-Darboux kernel
Swann Marx, Edouard Pauwels, Tillmann Weisser +2
We provide a new method to approximate a (possibly discontinuous) function using Christoffel-Darboux kernels. Our knowledge about the unknown multivariate function is in terms of f…