Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Abstract
Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.
Cite
@article{arxiv.2312.07615,
title = {Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings},
author = {Deep Chatterjee and Philip C. Harris and Maanas Goel and Malina Desai and Michael W. Coughlin and Erik Katsavounidis},
journal= {arXiv preprint arXiv:2312.07615},
year = {2023}
}
Comments
Accepted for Machine Learning and the Physical Sciences Workshop (submission 69) at NeurIPS 2023; for codes, see https://github.com/ML4GW/summer-projects-2023/blob/neurips-2023/symmetry-informed-flows/README.md