Scaling decision theoretic planning to large multiagent systems is challenging due to
uncertainty and partial observability in the environment. We focus on a multiagent planning …

We study multi-marginal optimal transport problems from a probabilistic graphical model
perspective. We point out an elegant connection between the two when the underlying cost …

Large‐scale monitoring of seasonal animal movement is integral to science, conservation
and outreach. However, gathering representative movement data across entire species …

Real-time spatiotemporal population data is attracting a great deal of attention for
understanding crowd movements in cities. The data is the aggregation of personal location …

We consider inference (filtering) problems over probabilistic graphical models with
aggregate data generated by a large population of individuals. We propose a new efficient …

Ecological inference (EI) is a classical problem from political science to model voting
behavior of individuals given only aggregate election results. Flaxman et al. recently …

We investigate the problem of learning discrete graphical models in a differentially private
way. Approaches to this problem range from privileged algorithms that conduct learning …

Thanks to the prevalence of mobile phones and GPS devices, spatiotemporal population
data can be obtained easily. In this article, we propose a mixture of collective graphical …

We propose a probabilistic model for estimating population flow, which is defined as
populations of the transition between areas over time, given aggregated spatio-temporal …

We study the problem of estimating latent population flows from aggregated count data. This
problem arises when individual trajectories are not available due to privacy issues or …