Title: Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme
Primary area: generative models
Keywords: population dynamics, JKO scheme, inverse problem

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.

TL;DR: We learn population dynamics by combining the JKO scheme with an inverse optimization framework.