3 papers
stat.ML2026
Efficient Stochastic Optimisation via Sequential Monte Carlo
James Cuin, Davide Carbone, Yanbo Tang +1
The problem of optimising functions with intractable gradients frequently arises in machine learning and statistics, ranging from maximum marginal likelihood estimation procedures…
cs.LG2026
Manifold Aware Denoising Score Matching (MAD)
Alona Levy-Jurgenson, Alvaro Prat, James Cuin +1
A major focus in designing methods for learning distributions defined on manifolds is to alleviate the need to implicitly learn the manifold so that learning can concentrate on the…
stat.CO2025
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
James Cuin, Davide Carbone, O. Deniz Akyildiz
We utilise a sampler originating from nonequilibrium statistical mechanics, termed here Jarzynski-adjusted Langevin algorithm (JALA), to build statistical estimation methods in lat…