Traditionally, the Bayesian optimal auction design problem has been
considered either when the bidder values are i.i.d, or when each bidder is
individually identifiable via her value distribution. The latter is a
reasonable approach when the bidders can be classified into a few categories,
but there are many instances where the classification of bidders is a
continuum. For example, the classification of the bidders may be based on their
annual income, their propensity to buy an item based on past behavior, or in
the case of ad auctions, the click through rate of their ads. We introduce an
alternate model that captures this aspect, where bidders are a priori
identical, but can be distinguished based (only) on some side information the
auctioneer obtains at the time of the auction. We extend the sample complexity
approach of Dhangwatnotai et al. and Cole and Roughgarden to this model and
obtain almost matching upper and lower bounds. As an aside, we obtain a revenue
monotonicity lemma which may be of independent interest. We also show how to
use Empirical Risk Minimization techniques to improve the sample complexity
bound of Cole and Roughgarden for the non-identical but independent value
distribution case.

本论文提出了一种新的拍卖模型，通过使用拍卖者在拍卖时获得的某些侧面信息来区分事先相同的竞标人，通过拓展 Dhangwatnotai et al. 和 Cole 和 Roughgarden 的样本复杂度方法来获得了几乎匹配的上限和下限，使用经验风险最小化技术来改进 Cole 和 Roughgarden 的样本复杂度界限。