@inproceedings{5e8ceba2ecd840d8b56b264f5aff891d,
title = "Sparse grid regression for interpretation of black box functions",
abstract = "Black box functions are often used by machine learning algorithms. These functions do not provide convenient way of analyzing sensitivity of response to input variables. This paper presents Sparse Grid Regression method to be used for converting black box function into a dimension-wise expansion model. Such model provides an excellent tool for interpretation and sensitivity analysis. A neural network was used as an example when comparing the novel Sparse Grid Regression method with commonly used quasi-Monte Carlo algorithm. A significant advantage in computational efficiency of the proposed Sparse Grid Regression method was observed.",
author = "Rastko Zivanovic",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th Mediterranean Conference on Control and Automation, MED 2021 ; Conference date: 22-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
day = "22",
doi = "10.1109/MED51440.2021.9480164",
language = "English",
series = "2021 29th Mediterranean Conference on Control and Automation, MED 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "765--769",
booktitle = "2021 29th Mediterranean Conference on Control and Automation, MED 2021",
address = "United States",
}