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Optimal No-regret Learning in Repeated First-price Auctions. (arXiv:2003.09795v5 [cs.LG] UPDATED)
July 18, 2022, 1:11 a.m. | Yanjun Han, Zhengyuan Zhou, Tsachy Weissman
stat.ML updates on arXiv.org arxiv.org
We study online learning in repeated first-price auctions with censored
feedback, where a bidder, only observing the winning bid at the end of each
auction, learns to adaptively bid in order to maximize her cumulative payoff.
To achieve this goal, the bidder faces a challenging dilemma: if she wins the
bid--the only way to achieve positive payoffs--then she is not able to observe
the highest bid of the other bidders, which we assume is iid drawn from an
unknown distribution. …
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