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

My H. Dinh, James Kotary, Ferdinando Fioretto

Research output: Contribution to journalConference Articlepeer-review

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 the optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.

Original languageEnglish (US)
Pages (from-to)1129-1145
Number of pages17
JournalProceedings of Machine Learning Research
Volume244
StatePublished - 2024
Externally publishedYes
Event40th Conference on Uncertainty in Artificial Intelligence, UAI 2024 - Barcelona, Spain
Duration: Jul 15 2024Jul 19 2024

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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