POMACS · 2026

Prediction-Specific Design of Learning-Augmented Algorithms

Sizhe Li, Nicolas Christianson, Tongxin Li

Proceedings of the ACM on Measurement and Analysis of Computing Systems

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Can an algorithm be optimal for the particular prediction it receives?

Strong optimality refines the usual consistency and robustness tradeoff to account for each prediction. A bilevel optimization framework constructs algorithms for ski rental and one max search, with case studies in power management and trading.

Research themes

Open questions

Prediction quality as a decision resource

Which prediction errors actually matter for the decision being made?

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Cite this paper

Sizhe Li, Nicolas Christianson, Tongxin Li. Prediction-Specific Design of Learning-Augmented Algorithms. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2026. https://doi.org/10.1145/3788100

BibTeX
@article{tongxin-prediction-specific,
  title = {{Prediction-Specific Design of Learning-Augmented Algorithms}},
  author = {Sizhe Li and Nicolas Christianson and Tongxin Li},
  year = {2026},
  journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
  url = {https://doi.org/10.1145/3788100},
  doi = {10.1145/3788100}
}

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