2024-09-12-dinh24a.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 Learning for Fair Multiobjective Optimization Under Uncertainty	Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize (PtO) paradigm in machine learning aims to maximize downstream decision quality by training the parametric inference model end-to-end with the subsequent constrained optimization. This requires backpropagation through the optimization problem using approximation techniques specific to the problem’s form, especially for nondifferentiable linear and mixed-integer programs. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives, known for their ability to ensure properties of fairness and robustness in decision models. Through a collection of training techniques and proposed application settings, it shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.	ptbCobFKtX	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	dinh24a	0	End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty	1129	1145	1129-1145	1129	false	Dinh, My H. and Kotary, James and Fioretto, Ferdinando	given family My H. Dinh given family James Kotary given family Ferdinando Fioretto	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/dinh24a/dinh24a.pdf