Related papers: End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty

URL: http://arxiv.org/abs/2402.07772v1
Date: Mon, 12 Feb 2024 16:33:35 GMT
Title: End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
Authors: My H Dinh and James Kotary and Ferdinando Fioretto
Abstract summary: The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
Score: 55.04219793298687
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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