Learning Pareto-Efficient Decisions with Confidence

@InProceedings{pmlr-v151-ek22a,
  title = 	 { Learning Pareto-Efficient Decisions with Confidence },
  author =       {Ek, Sofia and Zachariah, Dave and Stoica, Peter},
  booktitle = 	 {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {9969--9981},
  year = 	 {2022},
  editor = 	 {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
  volume = 	 {151},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {28--30 Mar},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v151/ek22a/ek22a.pdf},
  url = 	 {https://proceedings.mlr.press/v151/ek22a.html},
  abstract = 	 { The paper considers the problem of multi-objective decision support when outcomes are uncertain. We extend the concept of Pareto-efficient decisions to take into account the uncertainty of decision outcomes across varying contexts. This enables quantifying trade-offs between decisions in terms of tail outcomes that are relevant in safety-critical applications. We propose a method for learning efficient decisions with statistical confidence, building on results from the conformal prediction literature. The method adapts to weak or nonexistent context covariate overlap and its statistical guarantees are evaluated using both synthetic and real data. }
}