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Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

Authors Thomas Lavastida, Benjamin Moseley, R. Ravi, Chenyang Xu



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Author Details

Thomas Lavastida
  • Carnegie Mellon University, Pittsburgh, PA, USA
Benjamin Moseley
  • Carnegie Mellon University, Pittsburgh, PA, USA
R. Ravi
  • Carnegie Mellon University, Pittsburgh, PA, USA
Chenyang Xu
  • Zhejiang University, China

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Thomas Lavastida, Benjamin Moseley, R. Ravi, and Chenyang Xu. Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 59:1-59:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ESA.2021.59

Abstract

We propose a new model for augmenting algorithms with predictions by requiring that they are formally learnable and instance robust. Learnability ensures that predictions can be efficiently constructed from a reasonable amount of past data. Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific. Instance robustness insists on a smooth degradation in performance as a function of the change. Ideally, the performance is never worse than worst-case bounds. This also allows predictions to be objectively compared. We design online algorithms with predictions for a network flow allocation problem and restricted assignment makespan minimization. For both problems, two key properties are established: high quality predictions can be learned from a small sample of prior instances and these predictions are robust to errors that smoothly degrade as the underlying problem instance changes.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
Keywords
  • Learning-augmented algorithms
  • Online algorithms
  • Flow allocation

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