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(Dec. 3) Theoretical Foundations of Data-driven Auction Design

Last updated :2019-12-02

Topic: Theoretical Foundations of Data-driven Auction Design
Speaker: Dr. Zhiyi Huang
(The University of Hong Kong)
Host: Professor LI Lvzhou
Time: 15:00-16:00, Tuesday, December 3, 2019
Venue: A201, School of Data and Computer Science, Guangzhou East Campus, SYSU

Through a series of papers, we settle the sample complexity of single-parameter revenue maximizing auction design by showing matching upper and lower bounds, up to a poly-logarithmic factor, for all families of value distributions that have been considered in the literature. The upper bounds are unified under a novel framework, which builds on the strong revenue monotonicity by Devanur, Huang, and Psomas (STOC 2016), and an information theoretic argument. This is fundamentally different from the previous approaches that rely on either constructing an epsilon-net of the mechanism space, explicitly or implicitly via statistical learning theory, or learning an approximately accurate version of the virtual values. To our knowledge, it is the first time information theoretical arguments are used to show sample complexity upper bounds, instead of lower bounds.