11 papers
Diffusion Path Samplers via Sequential Monte Carlo
James Matthew Young, Paula Cordero-Encinar, Sebastian Reich +2
We develop diffusion-based samplers for target distributions known up to a normalising constant. To this end, we rely on the well-known diffusion path that smoothly interpolates be…
Gaussian entropic optimal transport: Schrödinger bridges and the Sinkhorn algorithm
O. Deniz Akyildiz, Pierre Del Moral, JoaquÃn Miguez
Entropic optimal transport problems are regularized versions of optimal transport problems. These models play an increasingly important role in machine learning and generative mode…
Sampling by averaging: A multiscale approach to score estimation
Paula Cordero-Encinar, Andrew B. Duncan, Sebastian Reich +1
We introduce a novel framework for efficient sampling from complex, unnormalised target distributions by exploiting multiscale dynamics. Traditional score-based sampling methods ei…
On diffusion posterior sampling via sequential Monte Carlo for zero-shot scaffolding of protein motifs
James Matthew Young, O. Deniz Akyildiz
With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The motif scaffolding problem requires steering this generative process to yield protei…
Uniform-in-time convergence bounds for Persistent Contrastive Divergence Algorithms
Paul Felix Valsecchi Oliva, O. Deniz Akyildiz, Andrew Duncan
We propose a continuous-time formulation of persistent contrastive divergence (PCD) for maximum likelihood estimation (MLE) of unnormalised densities. Our approach expresses PCD as…
Proximal Interacting Particle Langevin Algorithms
Paula Cordero Encinar, Francesca R. Crucinio, O. Deniz Akyildiz
We introduce a class of algorithms, termed proximal interacting particle Langevin algorithms (PIPLA), for inference and learning in latent variable models whose joint probability d…