469 citations · 2k across the 43 of their papers we have counts for
4 papers · 1 filter
Approximate Inference in Continuous Determinantal Point Processes
Raja Hafiz Affandi, Emily B. Fox, Ben Taskar
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset se…
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
Emily B. Fox, Michael C. Hughes, Erik B. Sudderth +1
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among t…
Spatio-Temporal Low Count Processes with Application to Violent Crime Events
Sivan Aldor-Noiman, Lawrence D. Brown, Emily B. Fox +1
There is significant interest in being able to predict where crimes will happen, for example to aid in the efficient tasking of police and other protective measures. We aim to mode…
A Bayesian approach for predicting the popularity of tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow
We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its r…