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Author

Vincent Pauline

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#machine learning Preprint Oct 2026

Amortised Inference through One-Step Implicit Sampling

Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple steps are usually required to draw unbiased samples, for example, iterations of an MCMC algorithm or integration steps in a diffusion-based amortised sampler. However,...

Vincent Pauline, Kirill Tamogashev, Arran Carter et al. · 0 citations
#artificial intelligence Preprint Oct 2026

One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control

This framework, Coordinated Multi-Agent Diffusion Steering (CMDS), treats frozen pretrained diffusion models as reusable generative primitives and coordinates their reverse processes through a learned control, forming coordination as a stochastic optimal control problem.

Riccardo Barbano, Vincent Pauline, Run-Chang Li et al. · 0 citations

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