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.STACS.2022.2
URN: urn:nbn:de:0030-drops-158127
URL: https://drops.dagstuhl.de/opus/volltexte/2022/15812/
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Balcan, Maria-Florina

Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk)

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LIPIcs-STACS-2022-2.pdf (0.2 MB)


Abstract

Many mechanisms including pricing mechanisms and auctions typically come with a variety of tunable parameters which impact significantly their desired performance guarantees. Data-driven mechanism design is a powerful approach for designing mechanisms, where these parameters are tuned via machine learning based on data. In this talk I will discuss how techniques from machine learning theory can be adapted and extended to analyze generalization guarantees of data-driven mechanism design.

BibTeX - Entry

@InProceedings{balcan:LIPIcs.STACS.2022.2,
  author =	{Balcan, Maria-Florina},
  title =	{{Generalization Guarantees for Data-Driven Mechanism Design}},
  booktitle =	{39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-222-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{219},
  editor =	{Berenbrink, Petra and Monmege, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/15812},
  URN =		{urn:nbn:de:0030-drops-158127},
  doi =		{10.4230/LIPIcs.STACS.2022.2},
  annote =	{Keywords: mechanism configuration, algorithm configuration, machine learning, generalization guarantees}
}

Keywords: mechanism configuration, algorithm configuration, machine learning, generalization guarantees
Collection: 39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022)
Issue Date: 2022
Date of publication: 09.03.2022


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