Learning Reserve Prices in Second-Price Auctions

This paper proves the tight sample complexity of Second-Price Auction with Anonymous Reserve, up to a logarithmic factor, for all value distribution families that have been considered in the literature. Compared to Myerson Auction, whose sample complexity was settled very recently in (Guo, Huang and Zhang, STOC 2019), Anonymous Reserve requires much fewer samples for learning. We follow a similar framework as the Guo-Huang-Zhang work, but replace their information theoretical argument with a direct proof

Optimal Auctions and Pricing with Limited Information

Information availability plays a fundamental role in decision-making for business operations. The present dissertation aims to develop frameworks and algorithms in order to guide a decision-maker in environments with limited information. In particular, in the first part, we study the fundamental problem of designing optimal auctions while relaxing the widely used assumption of common prior. We are able to characterize (near-)optimal mechanisms and associated performance. In the second part of the dissertation, we focus on data-driven pricing in the low sample regime. More precisely, we study the fundamental problem of a seller pricing a product based on historical information consisting of one sample of the willingness-to-pay distribution. By drawing connection with the statistical theory of reliability, we propose a novel approach, using dynamic programming, to characterize near-optimal data-driven pricing algorithms and their performance. In the last part of the dissertation, we delve into the detailed practical operations of the online display advertising marketplace from an information structure perspective. In particular, we analyze the tactical role of intermediaries within this marketplace and their impact on the value chain. In turn, we make the case that under some market conditions, there is a potential for Pareto improvement by adjusting the role of these intermediaries

Topics in Deep Learning and Data-driven Optimization

Data-driven optimization has become an increasingly popular approach for solving complex problems in various domains, such as finance, online retail, and engineering. However, in many real-world applications, the amount of available data can vary significantly, ranging from limited to large data sets. Both of these regimes present unique modeling and optimization challenges. In this thesis, we explore two distinct problems in two different data availability and model complexity regimes. In the first part (Chapters 2 and 3), we focus on the development of novel optimization algorithms for training deep neural network (DNN) models on large data sets, in particular, we develop practical optimization methods that incorporate curvature information in an economical way to accelerate the optimization process. The performance of the proposed methods is compared to that of several state-of-the-art methods used to train DNNs, to validate their effectiveness both in terms of time efficiency and generalization power. In the second part of the dissertation (Chapters 4), we focus on data-driven pricing in the limited data regime. More specifically, we study the fundamental problem of a seller pricing a product based on historical information consisting of the observed demand at a single historical price point. We develop a novel framework that allows characterizing optimal performance for deterministic or more general randomized mechanisms and leads to fundamental novel insights on the value of limited demand data for pricing