3 papers
stat.CO2026
A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo
Adrien Corenflos, Hai-Dang Dau
A long-standing gap exists between the theoretical analysis of Markov chain Monte Carlo convergence, which is often based on statistical divergences, and the diagnostics used in pr…
stat.ML2026
Accelerated Parallel Tempering via Neural Transports
Leo Zhang, Peter Potaptchik, Jiajun He +5
Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeti…
stat.ML2024
Particle Denoising Diffusion Sampler
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson +3
Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate sampl…