3 papers
cs.LG2026
Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation
Armand Rousselot, Joran Wendebourg, Ullrich Köthe
The performance of machine learning models is determined by the quality of their learned features. They should be invariant under irrelevant data variation but sensitive to task-re…
cs.LG2025
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
Stefan Wahl, Armand Rousselot, Felix Draxler +2
Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature…
cs.LG2024
Learning Distributions on Manifolds with Free-Form Flows
Peter Sorrenson, Felix Draxler, Armand Rousselot +2
We propose Manifold Free-Form Flows (M-FFF), a simple new generative model for data on manifolds. The existing approaches to learning a distribution on arbitrary manifolds are expe…