We propose a new approach to model the collective dynamics of a population of particles
evolving with time. As is often the case in challenging scientific applications, notably single …

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 …

During recent decades, there has been a substantial development in optimal mass transport
theory and methods. In this work, we consider multi-marginal problems wherein only partial …

The optimal transport problem has recently developed into a powerful framework for various
applications in estimation and control. Many of the recent advances in the theory and …

Abstract Multimarginal Optimal Transport (MOT) has attracted significant interest due to
applications in machine learning, statistics, and the sciences. However, in most applications …

Understanding how cells change their identity and behaviour in living systems is an
important question in many fields of biology. The problem of inferring cell trajectories from …

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

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 …

In this work, we develop a new framework for dynamic network flow problems based on
optimal transport theory. We show that the dynamic multicommodity minimum-cost network …

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