LIPIcs.STACS.2022.2.pdf
- Filesize: 250 kB
- 1 pages
Document
Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk)
Author
-
Part of:
Volume:
39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: Symposium on Theoretical Aspects of Computer Science (STACS) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-03-09
Cite AsGet BibTex
Maria-Florina Balcan. Generalization Guarantees for Data-Driven Mechanism Design (Invited Talk). In 39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 219, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.STACS.2022.2
https://doi.org/10.4230/LIPIcs.STACS.2022.2
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.
Subject Classification
ACM Subject Classification
- Theory of computation → Algorithmic game theory and mechanism design
- Theory of computation → Design and analysis of algorithms
Keywords
- mechanism configuration
- algorithm configuration
- machine learning
- generalization guarantees
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads