activity
20182026
most citedKernel Stein Discrepancy Descent

5 citations · 8 across the 18 of their papers we have counts for

collaborators

23 papers

math.OC2026

Fenchel-Young Duality Gaps: Certified Early Stopping for Regularized Inverse Problems

Pierre-Cyril Aubin-Frankowski, Yohann de Castro

We study computable error bounds and certified early stopping for regularized inverse problems, where a data-fidelity term is traded against a regularizer. The analysis relies on a…

cs.LG2026

Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization

Clément Bonet, Pierre-Cyril Aubin-Frankowski, Youssef Mroueh

Optimizing functionals over the space of probability measures is now ubiquitous in machine learning. A widely used approach is to perform the optimization directly over the Wassers…

math.MG2026

The Brezis-Ekeland-Nayroles principle in metric spaces: time-continuous setting

Pierre-Cyril Aubin-Frankowski, Giacomo Enrico Sodini, Ulisse Stefanelli

Based on a variational characterization of the local slope of a functional, we extend the celebrated Brezis-Ekeland-Nayroles null-minimization principle to curves of maximal slope…

math.OC2026

Debiasing optimal transport: classical and entropic

Pierre-Cyril Aubin-Frankowski, Virginie Ehrlacher, Gabriele Todeschi

We study the notion of debiasability for cost functions arising in optimal transport. We call a symmetric cost function …

math.FA2025

Evolution variational inequalities with general costs

Pierre-Cyril Aubin-Frankowski, Giacomo Enrico Sodini, Ulisse Stefanelli

We extend the theory of gradient flows beyond metric spaces by studying evolution variational inequalities (EVIs) driven by general cost functions , including Bregman and entrop…

stat.ML2024

Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems

Pierre-Cyril Aubin-Frankowski, Yohann De Castro, Axel Parmentier +1

Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A recent line of structured learning meth…