Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Machine Learning
2025-10-15 v1 Machine Learning
Computation
Abstract
We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developments in particle-based methods for solving maximum marginal likelihood estimation (MMLE) problems. Specifically, we provide a continuous-time framework for learning latent energy-based models, by defining stochastic differential equations (SDEs) that provably solve the MMLE problem. We obtain a practical algorithm as a discretisation of these SDEs and provide theoretical guarantees for the convergence of the proposed algorithm. Finally, we demonstrate the empirical effectiveness of our method on synthetic and image datasets.
Cite
@article{arxiv.2510.12311,
title = {Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics},
author = {Joanna Marks and Tim Y. J. Wang and O. Deniz Akyildiz},
journal= {arXiv preprint arXiv:2510.12311},
year = {2025}
}