Search Results for author: Matt Barnes

Found 11 papers, 5 papers with code

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

3 code implementations6 Jul 2020 Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, Shawn O'Banion

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.

Time Series Time Series Forecasting

Imitation Learning as $f$-Divergence Minimization

no code implementations30 May 2019 Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha Srinivasa

We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.

Imitation Learning

On the Interaction Effects Between Prediction and Clustering

1 code implementation18 Jul 2018 Matt Barnes, Artur Dubrawski

Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions.


Performance Bounds for Graphical Record Linkage

no code implementations8 Mar 2017 Rebecca C. Steorts, Matt Barnes, Willie Neiswanger

Record linkage involves merging records in large, noisy databases to remove duplicate entities.


Clustering on the Edge: Learning Structure in Graphs

no code implementations5 May 2016 Matt Barnes, Artur Dubrawski

With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples.

Clustering Entity Resolution +2

A Practioner's Guide to Evaluating Entity Resolution Results

1 code implementation14 Sep 2015 Matt Barnes

This paper provides practitioners the basic knowledge to begin evaluating their entity resolution results.

Clustering Entity Resolution +2

Performance Bounds for Pairwise Entity Resolution

no code implementations10 Sep 2015 Matt Barnes, Kyle Miller, Artur Dubrawski

One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.

BIG-bench Machine Learning Entity Resolution

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