3 papers
cs.LG2026
Amortised Inference through One-Step Implicit Sampling
Vincent Pauline, Kirill Tamogashev, Arran Carter +3
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple steps are usually required t…
cs.RO2026
One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control
Riccardo Barbano, Vincent Pauline, Runchang Li +6
Deep generative models often produce structured outputs composed of interacting components. Modelling these outputs with a single model requires learning both the component distrib…
stat.ML2025
Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline, Tobias Höppe, Kirill Neklyudov +3
Although diffusion models now occupy a central place in generative modeling, introductory treatments commonly assume Euclidean data and seldom clarify their connection to discrete-…