[en] Simulation-based inference methods have been shown to be inaccurate in the
data-poor regime, when training simulations are limited or expensive. Under
these circumstances, the inference network is particularly prone to
overfitting, and using it without accounting for the computational uncertainty
arising from the lack of identifiability of the network weights can lead to
unreliable results. To address this issue, we propose using Bayesian neural
networks in low-budget simulation-based inference, thereby explicitly
accounting for the computational uncertainty of the posterior approximation. We
design a family of Bayesian neural network priors that are tailored for
inference and show that they lead to well-calibrated posteriors on tested
benchmarks, even when as few as $O(10)$ simulations are available. This opens
up the possibility of performing reliable simulation-based inference using very
expensive simulators, as we demonstrate on a problem from the field of
cosmology where single simulations are computationally expensive. We show that
Bayesian neural networks produce informative and well-calibrated posterior
estimates with only a few hundred simulations.
Disciplines :
Computer science
Author, co-author :
Delaunoy, Arnaud ✱; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
de la Brassinne Bonardeaux, Maxence ✱
Mishra-Sharma, Siddharth
Louppe, Gilles ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Big Data
✱ These authors have contributed equally to this work.
Language :
English
Title :
Low-Budget Simulation-Based Inference with Bayesian Neural Networks
