Summary of End-to-end Learning For Fair Multiobjective Optimization Under Uncertainty, by My H Dinh and James Kotary and Ferdinando Fioretto
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
by My H Dinh, James Kotary, Ferdinando Fioretto
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Predict-Then-Optimize (PtO) paradigm in machine learning is designed to maximize downstream decision quality by training a parametric inference model end-to-end with subsequent constrained optimization. This approach requires backpropagation through the optimization problem using approximation techniques specific to the problem’s form, particularly for nondifferentiable linear and mixed-integer programs. The paper extends this methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives, which are known for their ability to ensure fairness and robustness in decision models. By integrating OWA function optimization with parametric prediction, the approach demonstrates effective fair and robust optimization under uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make better decisions using artificial intelligence and operations research. The goal is to find the best solution by combining two steps: predicting what might happen and then optimizing the decision based on that prediction. This helps ensure fairness and robustness in the decision-making process. The method can be applied to different types of problems, including those with uncertain outcomes. |
Keywords
» Artificial intelligence » Backpropagation » Inference » Machine learning » Optimization
