Particle-based Generalised Stochastic Optimisation
We develop a class of general 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 provide 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 adm...
| Authors | Jiechen Jackie Zhang, O. Deniz Akyildiz |
|---|---|
| Primary category | stat.ML |
| Published | 3 August 2026 |
| Last updated at source | 9 October 2026 |
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