Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference

Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, Francois Xavier Briol

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously been used for approximate Bayesian computation, minimum distance estimation, generalised Bayesian inference, and within the nonparametric learning framework. The MMD is commonly estimated at a root-m rate, where m is the number of simulated samples. This can lead to significant computational challenges since a large m is required to obtain an accurate estimate, which is crucial for parameter estimation. In this paper, we propose a novel estimator for the MMD with significantly improved sample complexity. The estimator is particularly well suited for computationally expensive smooth simulators with low- to mid-dimensional inputs. This claim is supported through both theoretical results and an extensive simulation study on benchmark simulators.
Original languageEnglish
Title of host publicationProceedings of the 40th International Conference on Machine Learning
EditorsAndread Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett
PublisherJMLR
Pages2289-2312
Number of pages24
Publication statusPublished - Jul 2023
MoE publication typeA4 Conference publication
EventInternational Conference on Machine Learning - Honolulu, United States
Duration: 23 Jul 202329 Jul 2023
Conference number: 40

Publication series

NameProceedings of Machine Learning Research
PublisherJMLR
Volume202
ISSN (Electronic)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
Country/TerritoryUnited States
CityHonolulu
Period23/07/202329/07/2023

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