2024-09-12-chenreddy24a.md

title	abstract	openreview	section	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
End-to-end Conditional Robust Optimization	The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety and reliability in high stake applications. Exploiting modern differentiable optimization methods, we propose a novel end-to-end approach to train a CRO model in a way that accounts for both the empirical risk of the prescribed decisions and the quality of conditional coverage of the contextual uncertainty set that supports them. While guarantees of success for the latter objective are impossible to obtain from the point of view of conformal prediction theory, high quality conditional coverage is achieved empirically by ingeniously employing a logistic regression differentiable layer within the calculation of coverage quality in our training loss.We show that the proposed training algorithms produce decisions that outperform the traditional estimate then optimize approaches.	Oe9ngGi8Gh	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chenreddy24a	0	End-to-end Conditional Robust Optimization	736	748	736-748	736	false	Chenreddy, Abhilash Reddy and Delage, Erick	given family Abhilash Reddy Chenreddy given family Erick Delage	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/chenreddy24a/chenreddy24a.pdf