2019/04/10 by Chenghao Guo, Zhiyi Huang, Xinzhi Zhang · 1 citation
Computer Science · Decision Sciences · #Auction Theory and Applications #Imbalanced Data Classification Techniques #Matching (statistics) #Maximization #Monotonic function #Optimization and Search Problems #Revenue #Sample (material) #Sample complexity #Upper and lower bounds #Value (mathematics) #cs.GT
paper · pdf · doi:10.1145/3313276.3316325
49 pages, Accepted by STOC19
arxiv created 2019/04/10 · arxiv updated 2019/04/11 · openalex created_date 2019/04/25 · openalex publication_date 2019/06/20 · openalex updated_date 2026/08/05
This paper settles the sample complexity of single-parameter revenue maximization 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 є-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. Our lower bounds are also unified under a meta construction of hard instances.