7 citations · 9 across the 8 of their papers we have counts for
10 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…
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…
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…
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…
A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations
Clement Guilloteau, Gavin Kerrigan, Kai Nelson +4
A generative diffusion model is used to produce probabilistic ensembles of precipitation intensity maps at the 1-hour 5-km resolution. The generation is conditioned on infrared and…
Dynamic Conditional Optimal Transport through Simulation-Free Flows
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
We study the geometry of conditional optimal transport (COT) and prove a dynamical formulation which generalizes the Benamou-Brenier Theorem. Equipped with these tools, we propose…
Functional Flow Matching
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
We propose Functional Flow Matching (FFM), a function-space generative model that generalizes the recently-introduced Flow Matching model to operate in infinite-dimensional spaces.…
Diffusion Generative Models in Infinite Dimensions
Gavin Kerrigan, Justin Ley, Padhraic Smyth
Diffusion generative models have recently been applied to domains where the available data can be seen as a discretization of an underlying function, such as audio signals or time…
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
Gavin Kerrigan, Padhraic Smyth, Mark Steyvers
An increasingly common use case for machine learning models is augmenting the abilities of human decision makers. For classification tasks where neither the human or model are perf…
Differentially Private Language Models Benefit from Public Pre-training
Gavin Kerrigan, Dylan Slack, Jens Tuyls
Language modeling is a keystone task in natural language processing. When training a language model on sensitive information, differential privacy (DP) allows us to quantify the de…