4 papers
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…
EventFlow: Forecasting Temporal Point Processes with Flow Matching
Gavin Kerrigan, Kai Nelson, Padhraic Smyth
Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling pa…
Guided Diffusion Sampling on Function Spaces with Applications to PDEs
Jiachen Yao, Abbas Mammadov, Julius Berner +4
We propose a general framework for conditional sampling in PDE-based inverse problems, targeting the recovery of whole solutions from extremely sparse or noisy measurements. This i…
A Geometric Approach to Optimal Experimental Design
Gavin Kerrigan, Christian A. Naesseth, Tom Rainforth
We introduce a novel geometric framework for optimal experimental design (OED). Traditional OED approaches, such as those based on mutual information, rely explicitly on probabilit…