2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
On backward smoothing algorithms
Hai-Dang Dau, Nicolas Chopin
In the context of state-space models, skeleton-based smoothing algorithms rely on a backward sampling step which by default has a complexity (where is the num…
Waste-free Sequential Monte Carlo
Hai-Dang Dau, Nicolas Chopin
A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be pe…