3 papers
cs.LG2026
Stochastic Adaptive Gradient Descent Without Descent
Jean-François Aujol, Jérémie Bigot, Camille Castera
We introduce a new adaptive step-size strategy for convex optimization with stochastic gradient that exploits the local geometry of the objective function only by means of a first-…
math.OC2026
On the Convergence of Proximal Algorithms for Weakly-convex Min-max Optimization
Guido Tapia-Riera, Camille Castera, Nicolas Papadakis
We study alternating first-order algorithms with no inner loops for solving nonconvex-strongly-concave min-max problems. We show the convergence of the alternating gradient descent…
cs.LG2025
From Learning to Optimize to Learning Optimization Algorithms
Camille Castera, Peter Ochs
Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not…