BibTeX

@article{2312.05440v3,
Author        = {Marvin Schmitt and Valentin Pratz and Ullrich Köthe and Paul-Christian Bürkner and Stefan T Radev},
Title         = {Consistency Models for Scalable and Fast Simulation-Based Inference},
Eprint        = {2312.05440v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Simulation-based inference (SBI) is constantly in search of more expressive
and efficient algorithms to accurately infer the parameters of complex
simulation models. In line with this goal, we present consistency models for
posterior estimation (CMPE), a new conditional sampler for SBI that inherits
the advantages of recent unconstrained architectures and overcomes their
sampling inefficiency at inference time. CMPE essentially distills a continuous
probability flow and enables rapid few-shot inference with an unconstrained
architecture that can be flexibly tailored to the structure of the estimation
problem. We provide hyperparameters and default architectures that support
consistency training over a wide range of different dimensions, including
low-dimensional ones which are important in SBI workflows but were previously
difficult to tackle even with unconditional consistency models. Our empirical
evaluation demonstrates that CMPE not only outperforms current state-of-the-art
algorithms on hard low-dimensional benchmarks, but also achieves competitive
performance with much faster sampling speed on two realistic estimation
problems with high data and/or parameter dimensions.},
Year          = {2023},
Month         = {Dec},
Note          = {Neural Information Processing Systems (NeurIPS 2024)},
Url           = {http://arxiv.org/abs/2312.05440v3},
File          = {2312.05440v3.pdf}
}