{"ID":2855654,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12311","arxiv_id":"2510.12311","title":"Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.12311","url_pdf":"https://arxiv.org/pdf/2510.12311v1","authors":"[\"Joanna Marks\",\"Tim Y. J. Wang\",\"O. Deniz Akyildiz\"]","published":"2025-10-14T09:10:37Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"stat.CO\"]","methods":"[]","has_code":false}