Google Research

Learning to Bid in Contextual First Price Auctions

EC 2021 conference submission (2021)


In this paper, we investigate the problem about how to bid in repeated contextual first price auctions. We consider a single bidder (learner) who repeatedly bids in the first price auctions: at each time t, the learner observes a context xt ∈ R d and decides the bid based on historical information and xt. We assume a structured linear model of the maximum bid of all the others mt = α0 · xt + zt, where α0 ∈ R d is unknown to the learner and zt is randomly sampled from a noise distribution F with log-concave density function f. We consider both binary feedback (the learner can only observe whether she wins or not) and full information feedback (the learner can observe mt) at the end of each time t. For binary feedback, when the noise distribution F is known, we propose a bidding algorithm, by using maximum likelihood estimation (MLE) method to achieve at most Oe( p log(d)T ) regret. Moreover, we generalize this algorithm to the setting with binary feedback and the noise distribution is unknown but belongs to a parametrized family of distributions. For the full information feedback with unknown noise distribution, we provide an algorithm that achieves regret at most Oe( √ dT). Our approach combines an estimator for log-concave density functions and then MLE method to learn the noise distribution F and linear weight α0 simultaneously. We also provide a lower bound result such that any bidding policy in a broad class must achieve regret at least Ω(√ T), even when the learner receives the full information feedback and F is known.

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