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

Author Maria-Florina Balcan

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Author Details

Maria-Florina Balcan
  • Carnegie Mellon University, Pittsburgh, PA, USA

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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)


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
  • mechanism configuration
  • algorithm configuration
  • machine learning
  • generalization guarantees


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