Learning to Bid in Repeated First-price Auctions

Learning to Bid in Repeated First-price Auctions
Friday, September 18, 2020 - 1:15pm
Yanjun Han (Stanford)
Abstract / Description: 

First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction where it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors? To answer this question, we study online learning in repeated first-price auctions, and consider various scenarios involving different assumptions on the characteristics of the other bidders' bids, of the bidder's private valuation, of the feedback structure of the auction, and of the reference policies with which our bidder competes. For all of them, we characterize the essentially optimal performance and identify computationally efficient algorithms achieving it. Experimentation on first-price auction datasets from Verizon Media demonstrates the promise of our schemes relative to existing bidding algorithms.

Based on joint work with Aaron Flores, Erik Ordentlich, Tsachy Weissman, and Zhengyuan Zhou. The full papers are available online at https://arxiv.org/abs/2003.09795 and https://arxiv.org/abs/2007.04568


The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held every Friday at 1:15 pm during the academic year.

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Yanjun Han is a 6th-year PhD candidate in Electrical Engineering at Stanford University, advised by Tsachy Weissman. His research interests include high-dimensional and nonparametric statistics, information theory, online learning, applied probability, and their applications.