3 papers
stat.ML2026
Bayesian Experimental Design via Score Matching
Angus Phillips, Gavin Kerrigan, Tom Rainforth
Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously colle…
stat.ML2026
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Tom Rossa, Angus Phillips, Tom Rainforth
Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it…
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…