3 papers
cs.LG2026
Efficient Learning of Deep State Space Models via Importance Smoothing
John-Joseph Brady, Nikolas Nusken, Yunpeng Li
Latent state space systems are ubiquitous in statistical modelling, arising naturally when time series are observed through noisy measurements. However, training deep state space m…
stat.ML2025
Conditioning Diffusions Using Malliavin Calculus
Jakiw Pidstrigach, Elizabeth Baker, Carles Domingo-Enrich +2
In generative modelling and stochastic optimal control, a central computational task is to modify a reference diffusion process to maximise a given terminal-time reward. Most exist…
stat.ML2025
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing +1
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path sp…