Implicit Diffusion: Efficient Optimization Through Stochastic Sampling
Abstract
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling processes by optimizing over their parameters. We introduce a general framework for first-order optimization of these processes, that performs jointly, in a single loop, optimization and sampling steps. We showcase it in training and finetuning applications.
Cite
Text
Marion et al. "Implicit Diffusion: Efficient Optimization Through Stochastic Sampling." ICML 2024 Workshops: Differentiable_Almost_Everything, 2024.Markdown
[Marion et al. "Implicit Diffusion: Efficient Optimization Through Stochastic Sampling." ICML 2024 Workshops: Differentiable_Almost_Everything, 2024.](https://mlanthology.org/icmlw/2024/marion2024icmlw-implicit/)BibTeX
@inproceedings{marion2024icmlw-implicit,
title = {{Implicit Diffusion: Efficient Optimization Through Stochastic Sampling}},
author = {Marion, Pierre and Korba, Anna and Bartlett, Peter and Blondel, Mathieu and De Bortoli, Valentin and Doucet, Arnaud and Llinares-López, Felipe and Paquette, Courtney and Berthet, Quentin},
booktitle = {ICML 2024 Workshops: Differentiable_Almost_Everything},
year = {2024},
url = {https://mlanthology.org/icmlw/2024/marion2024icmlw-implicit/}
}