End-to-end Conditional Robust Optimization

Abhilash Reddy Chenreddy, Erick Delage
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:736-748, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-chenreddy24a, title = {End-to-end Conditional Robust Optimization}, author = {Chenreddy, Abhilash Reddy and Delage, Erick}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {736--748}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/chenreddy24a/chenreddy24a.pdf}, url = {https://proceedings.mlr.press/v244/chenreddy24a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T End-to-end Conditional Robust Optimization %A Abhilash Reddy Chenreddy %A Erick Delage %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-chenreddy24a %I PMLR %P 736--748 %U https://proceedings.mlr.press/v244/chenreddy24a.html %V 244 %X 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.
APA
Chenreddy, A.R. & Delage, E.. (2024). End-to-end Conditional Robust Optimization. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:736-748 Available from https://proceedings.mlr.press/v244/chenreddy24a.html.

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