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DOI: 10.4230/LIPIcs.STACS.2022.2
URN: urn:nbn:de:0030-drops-158127
URL: https://drops.dagstuhl.de/opus/volltexte/2022/15812/
Balcan, Maria-Florina
Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk)
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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 | |
| Seminar: | 39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022) | |
| Issue date: | 2022 | |
| Date of publication: | 09.03.2022 |
