45 citations · 50 across the 10 of their papers we have counts for
5 papers · 1 filter
Benchmark Data Repositories for Better Benchmarking
Rachel Longjohn, Markelle Kelly, Sameer Singh +1
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- a…
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning, Eric Nalisnick, Christophe Ley +2
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers…
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…