AI RESEARCH
Joint Value Estimation and Bidding in Repeated First-Price Auctions
arXiv CS.LG
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ArXi:2502.17292v3 Announce Type: replace We study regret minimization in repeated first-price auctions (FPAs), where a bidder observes only the realized outcome after each auction -- win or loss. This setup reflects practical scenarios in online display advertising where the actual value of an impression depends on the difference between two potential outcomes, such as clicks or conversion rates, when the auction is won versus lost.