Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme
Abstract
Learning population dynamics involves recovering the underlying process that governs particle evolution, given evolutionary snapshots of samples at discrete time points. Recent methods frame this as an energy minimization problem in probability space and leverage the celebrated JKO scheme for efficient time discretization. In this work, we introduce ``iJKOnet``, an approach that combines the JKO framework with inverse optimization techniques to learn population dynamics. Our method relies on a conventional *end-to-end* adversarial training procedure and does not require restrictive architectural choices, e.g., input-convex neural networks. We establish theoretical guarantees for our methodology and demonstrate improved performance over prior JKO-based methods.
Cite
Text
Persiianov et al. "Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme." International Conference on Learning Representations, 2026.Markdown
[Persiianov et al. "Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme." International Conference on Learning Representations, 2026.](https://mlanthology.org/iclr/2026/persiianov2026iclr-learning/)BibTeX
@inproceedings{persiianov2026iclr-learning,
title = {{Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme}},
author = {Persiianov, Mikhail and Chen, Jiawei and Mokrov, Petr and Tyurin, Alexander and Burnaev, Evgeny and Korotin, Alexander},
booktitle = {International Conference on Learning Representations},
year = {2026},
url = {https://mlanthology.org/iclr/2026/persiianov2026iclr-learning/}
}