# Mixtures of ensembles: System separation and identification via optimal transport

arXiv: https://arxiv.org/abs/2503.13362 · HTML: https://mlsanity.com/paper/2503.13362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Adjustment of Cluster-Then-Predict Framework for Multiport Scatterer Load Prediction](https://mlsanity.com/paper/2602.08129/) (2026)

## Abstract (arXiv)

Crowd dynamics and many large biological systems can be described as populations of agents or particles, which can only be observed on aggregate population level. Identifying the dynamics of agents is crucial for understanding these large systems. However, the population of agents is typically not homogeneous, and thus the aggregate observations consist of the superposition of multiple ensembles each governed by individual dynamics. In this work, we propose an optimal transport framework to jointly separate the population into several ensembles and identify each ensemble's dynamical system, based on aggregate observations of the population. We propose a bi-convex optimization problem, which we solve using a block coordinate descent with convergence guarantees. In numerical experiments, we demonstrate that the proposed approach exhibits close-to-oracle performance also in noisy settings, yielding accurate estimates of both the ensembles and the parameters governing their dynamics.