1 Sitaatiot (Scopus)
68 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 40th International Conference on Machine Learning
ToimittajatAndread Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett
KustantajaJMLR
Sivut2289-2312
Sivumäärä24
TilaJulkaistu - heinäk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Machine Learning - Honolulu, Yhdysvallat
Kesto: 23 heinäk. 202329 heinäk. 2023
Konferenssinumero: 40

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaJMLR
Vuosikerta202
ISSN (elektroninen)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
LyhennettäICML
Maa/AlueYhdysvallat
KaupunkiHonolulu
Ajanjakso23/07/202329/07/2023

Sormenjälki

Sukella tutkimusaiheisiin 'Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

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