2022-06-28-bharti22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Approximate Bayesian Computation with Domain Expert in the Loop	Proceedings of the 39th International Conference on Machine Learning	Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of information and dimensionality reduction, and is often determined based on domain knowledge. However, handcrafting and selecting suitable statistics is a laborious task involving multiple trial-and-error steps. In this work, we introduce an active learning method for ABC statistics selection which reduces the domain expert’s work considerably. By involving the experts, we are able to handle misspecified models, unlike the existing dimension reduction methods. Moreover, empirical results show better posterior estimates than with existing methods, when the simulation budget is limited.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bharti22a	0	Approximate {B}ayesian Computation with Domain Expert in the Loop	1893	1905	1893-1905	1893	false	Bharti, Ayush and Filstroff, Louis and Kaski, Samuel	given family Ayush Bharti given family Louis Filstroff given family Samuel Kaski	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bharti22a/bharti22a.pdf