Optimal No-Regret Learning in Repeated First-Price Auctions

In “Optimal No-Regret Learning in Repeated First-Price Auctions,” Y. Han, W. Tsachy, and Z. Zhou study online learning in repeated first-price auctions where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid to maximize her cumulative payoff. To achieve th...

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Veröffentlicht in:	Operations research 2024-07
Hauptverfasser:	Han, Yanjun, Weissman, Tsachy, Zhou, Zhengyuan
Format:	Artikel
Sprache:	eng
Schlagworte:	censored demand first-price auction graph feedback Machine Learning and Data Science online learning regret analysis
Online-Zugang:	Volltext
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