2023-08-17-alkhatib23a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Approximating Score-based Explanation Techniques Using Conformal Regression	Score-based explainable machine-learning techniques are often used to understand the logic behind black-box models. However, such explanation techniques are often computationally expensive, which limits their application in time-critical contexts. Therefore, we propose and investigate the use of computationally less costly regression models for approximating the output of score-based explanation techniques, such as SHAP. Moreover, validity guarantees for the approximated values are provided by the employed inductive conformal prediction framework. We propose several non-conformity measures designed to take the difficulty of approximating the explanations into account while keeping the computational cost low. We present results from a large-scale empirical investigation, in which the approximate explanations generated by our proposed models are evaluated with respect to efficiency (interval size). The results indicate that the proposed method can significantly improve execution time compared to the fast version of SHAP, TreeSHAP. The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees. Moreover, the proposed approach allows for comparing explanations of different approximation methods and selecting a method based on how informative (tight) are the predicted intervals.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	alkhatib23a	0	Approximating Score-based Explanation Techniques Using Conformal Regression	450	469	450-469	450	false	Alkhatib, Amr and Bostrom, Henrik and Ennadir, Sofiane and Johansson, Ulf	given family Amr Alkhatib given family Henrik Bostrom given family Sofiane Ennadir given family Ulf Johansson	2023-08-17		Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications	204	inproceedings	date-parts 2023 8 17	https://proceedings.mlr.press/v204/alkhatib23a/alkhatib23a.pdf