150 citations · 162 across the 7 of their papers we have counts for
7 papers
Massively Scalable Inverse Reinforcement Learning in Google Maps
Matt Barnes, Matthew Abueg, Oliver F. Lange +5
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addre…
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Amol Kapoor, Xue Ben, Luyang Liu +4
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecastin…
On the Interaction Effects Between Prediction and Clustering
Matt Barnes, Artur Dubrawski
Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects betw…
Performance Bounds for Graphical Record Linkage
Rebecca C. Steorts, Matt Barnes, Willie Neiswanger
Record linkage involves merging records in large, noisy databases to remove duplicate entities. It has become an important area because of its widespread occurrence in bibliometric…
Clustering on the Edge: Learning Structure in Graphs
Matt Barnes, Artur Dubrawski
With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples. We show how learning cluster structure using edge…
A Practioner's Guide to Evaluating Entity Resolution Results
Matt Barnes
Entity resolution (ER) is the task of identifying records belonging to the same entity (e.g. individual, group) across one or multiple databases. Ironically, it has multiple names:…