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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

Funding

Thomas Lavastida, and Benjamin Moseley: Supported in part by a Google Research Award, an Infor Research Award, a Carnegie Bosch Junior Faculty Chair and NSF grants CCF-1824303, CCF-1845146, CCF-1733873 and CMMI-1938909.
  • Ravi, R.: Supported in part by the U. S. Office of Naval Research under award number N00014-21-1-2243 and the Air Force Office of Scientific Research under award number FA9550-20-1-0080.
  • Xu, Chenyang: Supported in part by Science and Technology Innovation 2030 - "The Next Generation of Artificial Intelligence" Major Project No.2018AAA0100902 and China Scholarship Council No.201906320329.

Cite AsGet BibTex

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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