Projekteja vuodessa
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.
Alkuperäiskieli | Englanti |
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Otsikko | Proceedings of the 40th International Conference on Machine Learning |
Toimittajat | Andread Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett |
Kustantaja | JMLR |
Sivut | 2289-2312 |
Sivumäärä | 24 |
Tila | Julkaistu - heinäk. 2023 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
Tapahtuma | International Conference on Machine Learning - Honolulu, Yhdysvallat Kesto: 23 heinäk. 2023 → 29 heinäk. 2023 Konferenssinumero: 40 |
Julkaisusarja
Nimi | Proceedings of Machine Learning Research |
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Kustantaja | JMLR |
Vuosikerta | 202 |
ISSN (elektroninen) | 2640-3498 |
Conference
Conference | International Conference on Machine Learning |
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Lyhennettä | ICML |
Maa/Alue | Yhdysvallat |
Kaupunki | Honolulu |
Ajanjakso | 23/07/2023 → 29/07/2023 |
Sormenjälki
Sukella tutkimusaiheisiin 'Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
- 1 Päättynyt
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-: Finnish Center for Artificial Intelligence
Kaski, S. (Vastuullinen tutkija)
01/01/2019 → 31/12/2022
Projekti: Academy of Finland: Other research funding