License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2021.59
URN: urn:nbn:de:0030-drops-146405
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14640/
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Lavastida, Thomas ; Moseley, Benjamin ; Ravi, R. ; Xu, Chenyang

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

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LIPIcs-ESA-2021-59.pdf (0.7 MB)

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.

BibTeX - Entry

@InProceedings{lavastida_et_al:LIPIcs.ESA.2021.59,
  author =	{Lavastida, Thomas and Moseley, Benjamin and Ravi, R. and Xu, Chenyang},
  title =	{{Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing}},
  booktitle =	{29th Annual European Symposium on Algorithms (ESA 2021)},
  pages =	{59:1--59:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-204-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{204},
  editor =	{Mutzel, Petra and Pagh, Rasmus and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14640},
  URN =		{urn:nbn:de:0030-drops-146405},
  doi =		{10.4230/LIPIcs.ESA.2021.59},
  annote =	{Keywords: Learning-augmented algorithms, Online algorithms, Flow allocation}
}

Keywords: Learning-augmented algorithms, Online algorithms, Flow allocation
Collection: 29th Annual European Symposium on Algorithms (ESA 2021)
Issue Date: 2021
Date of publication: 31.08.2021

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