7 citations · 19 across the 12 of their papers we have counts for
11 papers · 1 filter
Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems
Jérôme Bolte, Edouard Pauwels, Antonio Silveti-Falls
We leverage path differentiability and a recent result on nonsmooth implicit differentiation calculus to give sufficient conditions ensuring that the solution to a monotone inclusi…
Path differentiability of ODE flows
Swann Marx, Edouard Pauwels
We consider flows of ordinary differential equations (ODEs) driven by path differentiable vector fields. Path differentiable functions constitute a proper subclass of Lipschitz fun…
Nonsmooth Implicit Differentiation for Machine Learning and Optimization
Jérôme Bolte, Tam Le, Edouard Pauwels +1
In view of training increasingly complex learning architectures, we establish a nonsmooth implicit function theorem with an operational calculus. Our result applies to most practic…
Second-order step-size tuning of SGD for non-convex optimization
Camille Castera, Jérôme Bolte, Cédric Févotte +1
In view of a direct and simple improvement of vanilla SGD, this paper presents a fine-tuning of its step-sizes in the mini-batch case. For doing so, one estimates curvature, based…
A Hölderian backtracking method for min-max and min-min problems
Jérôme Bolte, Lilian Glaudin, Edouard Pauwels +1
We present a new algorithm to solve min-max or min-min problems out of the convex world. We use rigidity assumptions, ubiquitous in learning, making our method applicable to many o…
Incremental Without Replacement Sampling in Nonconvex Optimization
Edouard Pauwels
Minibatch decomposition methods for empirical risk minimization are commonly analysed in a stochastic approximation setting, also known as sampling with replacement. On the other h…