Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye
arXiv 11 Jun 2020 · cs.GT · 6 citations (OpenAlex)
arXiv:2006.06519 · PDF · DOI · OpenAlex · Extracted main text
The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders' responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a demand component and a bidding component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from Google ad exchange.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. Liu, X. Li, P. Chen, J. Haupt, and L. Amini (2018) Zeroth-order stochastic projected gradient descent for nonconvex optimization | 0.737 | 3 | 3 | 67% |
| 2 | Krishnakumar Balasubramanian and Saeed Ghadimi (2018) Zeroth-order nonconvex stochastic optimization: Handling constraints, high-dimensionality and saddle-points, 2018 | 0.737 | 3 | 2 | 100% |
| 3 | Saeed. Ghadimi and Guanghui. Lan (2013) Stochastic first- and zeroth-order methods for nonconvex stochastic programming | 0.737 | 3 | 2 | 100% |
| 4 | Vijay Krishna (2009) Auction theory | 0.737 | 3 | 2 | 100% |
| 5 | Saeed Ghadimi (2019) Conditional gradient type methods for composite nonlinear and stochastic optimization | 0.644 | 2 | 2 | 100% |
| 6 | Steven A. Matthews (1995) A Technical Primer on Auction Theory I: Independent Private Values | 0.644 | 2 | 2 | 100% |
| 7 | Mehryar Mohri and Andrés Muñoz Medina (2016) Learning algorithms for second-price auctions with reserve | 0.511 | 2 | 1 | 100% |
| 8 | Andres Munoz and Sergei Vassilvitskii (2017) Revenue optimization with approximate bid predictions | 0.511 | 2 | 1 | 100% |
| 9 | R. Myerson (1981) Optimal auction design | 0.511 | 2 | 1 | 100% |
| 10 | Alekh Agarwal, Ofer Dekel, and Lin Xiao (2010) Optimal algorithms for online convex optimization with multi-point bandit feedback | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 30 scored citations.