Particle-based Generalised Stochastic Optimisation

Jiechen Jackie Zhang; O. Deniz Akyildiz

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arXiv · 2608.02844

Particle-based Generalised Stochastic Optimisation

Jiechen Jackie Zhang·O. Deniz Akyildiz

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Abstract

We develop a class of diffusion-based stochastic particle optimisation methods for loss functions with intractable gradients. Specifically, we consider problems in which the loss gradient is an integral with respect to a parameter-dependent distribution, a structure that includes training generative models, fine-tuning, and learning latent-variable models. We introduce mean-field dynamics and its interacting-particle approximations, which contain several existing algorithms as special cases and provides a route to constructing new methods. Under well-posedness and joint contractivity assumptions, we prove exponential convergence and show that the continuous-time particle system admits a non-asymptotic error bound. We illustrate it by developing momentum and higher-order Langevin variants and evaluating them on maximum marginal-likelihood estimation and energy-based-model training.

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BibTeX RIS

Jiechen Jackie Zhang, O. Deniz Akyildiz. 2026-08-03. Particle-based Generalised Stochastic Optimisation. https://arxiv.org/abs/2608.02844

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