19611

Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy

APA

(2022). Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/theoretical-foundations-data-driven-algorithm-design

MLA

Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy. The Simons Institute for the Theory of Computing, Feb. 11, 2022, https://simons.berkeley.edu/talks/theoretical-foundations-data-driven-algorithm-design

BibTex

          @misc{ scivideos_19611,
            doi = {},
            url = {https://simons.berkeley.edu/talks/theoretical-foundations-data-driven-algorithm-design},
            author = {},
            keywords = {},
            language = {en},
            title = {Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {feb},
            note = {19611 see, \url{https://scivideos.org/Simons-Institute/19611}}
          }
          
Ellen Vitercik (UC Berkeley)
Talk number19611
Source RepositorySimons Institute

Abstract

Machine learning is increasingly being used for mechanism design, with applications such as price optimization on online marketplaces and ad auction design. In this talk, I will give an overview of my research on mechanism design via machine learning, touching on statistical problems such as overfitting, incentive problems, and privacy preservation.