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stat.ML2026
A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
Samuel Howard, Nikolas Nüsken
Diffusion models are increasingly used as controllable samplers, whose generations can be steered at inference time according to a chosen reward function. While such rewards are ty…
stat.ML2026
Control Consistency Losses for Diffusion Bridges
Samuel Howard, Nikolas Nüsken, Jakiw Pidstrigach
Simulating the conditioned dynamics of diffusion processes, given their initial and terminal states, is an important but challenging problem in the sciences. The difficulty is part…
stat.ML2024
Stein transport for Bayesian inference
Nikolas Nüsken
We introduce , a novel methodology for Bayesian inference designed to efficiently push an ensemble of particles along a predefined curve of tempered proba…