4 papers
Amortised adaptive importance sampling
Arran Carter, Víctor Elvira, Esmeralda S. Whitammer
Monte Carlo sampling algorithms aim to produce samples from a target distribution with a given unnormalised probability density function. The high cost of running Monte Carlo algor…
Amortised Inference through One-Step Implicit Sampling
Vincent Pauline, Kirill Tamogashev, Arran Carter +3
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple steps are usually required t…
Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces
Arran Carter, Sanghyeok Choi, Kirill Tamogashev +2
Sampling from a distribution known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the…
Partial Trace-Class Bayesian Neural Networks
Arran Carter, Torben Sell
Bayesian neural networks (BNNs) allow rigorous uncertainty quantification in deep learning, but often come at a prohibitive computational cost. We propose three different innovativ…