2026-07-23T04:54:16Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:15812 2024-03-06T10:56:14Z ddc:004 open_access

Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk) Balcan, Maria-Florina mechanism configuration algorithm configuration machine learning generalization guarantees 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Maria-Florina Balcan 2022 Is Part Of LIPIcs, Volume 219, 39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.STACS.2022.2 urn:nbn:de:0030-drops-158127 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2022.2 eng https://creativecommons.org/licenses/by/4.0/legalcode