convergence analysis 1distribution matching 1gaussian distributions 1optimal transport 1sliced wasserstein 1
From the 1 of 3 papers with an AI index.
6 citations
3 papers
stat.ML2026
Convergence Rates for Distribution Matching with Sliced Optimal Transport
Gauthier Thurin, Claire Boyer, Kimia Nadjahi
The paper analyzes an iterative sliced optimal transport method for matching probability distributions, providing non‑asymptotic convergence rates and showing how the method behave…
math.ST2026
Confidence envelopes for the false discoveries with heterogeneous data
Romain Périer, Gilles Blanchard, Sebastian Döhler +2
In the context of selective inference, confidence envelopes for the false discoveries allow the user to select any subset of null hypotheses while having a statistical guarantee on…
math.ST2026★ 6 cited
On the convergence of PINNs
Nathan Doumèche, Gérard Biau, Claire Boyer
Physics-informed neural networks (PINNs) are a promising approach that combines the power of neural networks with the interpretability of physical modeling. PINNs have shown good p…