7 citations · 7 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…