Simulator Calibration under Covariate Shift with Kernels

Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1244-1253, 2020.

Abstract

We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embedding of distributions, and on the use of an importance-weighted reproducing kernel for covariate shift adaptation.We provide a theoretical analysis for the proposed method, including a novel theoretical result for conditional mean embedding, as well as empirical investigations suggesting its effectiveness in practice.The experiments include calibration of a widely used simulator for industrial manufacturing processes, where we also demonstrate how the proposed method may be useful for sensitivity analysis of model parameters.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-kisamori20a, title = {Simulator Calibration under Covariate Shift with Kernels}, author = {Kisamori, Keiichi and Kanagawa, Motonobu and Yamazaki, Keisuke}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1244--1253}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/kisamori20a/kisamori20a.pdf}, url = {https://proceedings.mlr.press/v108/kisamori20a.html}, abstract = {We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embedding of distributions, and on the use of an importance-weighted reproducing kernel for covariate shift adaptation.We provide a theoretical analysis for the proposed method, including a novel theoretical result for conditional mean embedding, as well as empirical investigations suggesting its effectiveness in practice.The experiments include calibration of a widely used simulator for industrial manufacturing processes, where we also demonstrate how the proposed method may be useful for sensitivity analysis of model parameters.} }
Endnote
%0 Conference Paper %T Simulator Calibration under Covariate Shift with Kernels %A Keiichi Kisamori %A Motonobu Kanagawa %A Keisuke Yamazaki %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-kisamori20a %I PMLR %P 1244--1253 %U https://proceedings.mlr.press/v108/kisamori20a.html %V 108 %X We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embedding of distributions, and on the use of an importance-weighted reproducing kernel for covariate shift adaptation.We provide a theoretical analysis for the proposed method, including a novel theoretical result for conditional mean embedding, as well as empirical investigations suggesting its effectiveness in practice.The experiments include calibration of a widely used simulator for industrial manufacturing processes, where we also demonstrate how the proposed method may be useful for sensitivity analysis of model parameters.
APA
Kisamori, K., Kanagawa, M. & Yamazaki, K.. (2020). Simulator Calibration under Covariate Shift with Kernels. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1244-1253 Available from https://proceedings.mlr.press/v108/kisamori20a.html.

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