This paper proposes the conformal online auction design (COAD), a novel
mechanism for maximizing revenue in online auctions by quantifying the
uncertainty in bidders' values without relying on assumptions about value
distributions. COAD incorporates both the bidder and item features and
leverages historical data to provide an incentive-compatible mechanism for
online auctions. Unlike traditional methods for online auctions, COAD employs a
distribution-free, prediction interval-based approach using conformal
prediction techniques. This novel approach ensures that the expected revenue
from our mechanism can achieve at least a constant fraction of the revenue
generated by the optimal mechanism. Additionally, COAD admits the use of a
broad array of modern machine-learning methods, including random forests,
kernel methods, and deep neural nets, for predicting bidders' values. It
ensures revenue performance under any finite sample of historical data.
Moreover, COAD introduces bidder-specific reserve prices based on the lower
confidence bounds of bidders' valuations, which is different from the uniform
reserve prices commonly used in the literature. We validate our theoretical
predictions through extensive simulations and a real-data application. All code
for using COAD and reproducing results is made available on GitHub.

基于预测区间的方法，提出了符合在线拍卖设计 (COAD)，以量化竞标者价值的不确定性为基础，实现在线拍卖的最大化收益。COAD 结合了竞标者和物品特征，并利用历史数据提供一种激励兼容的机制。与传统在线拍卖方法不同，COAD 采用无分布、基于预测区间的方法，使用一种新颖的符合预测技术。这种方法确保我们的机制所期望的收益至少能达到最优机制生成的收益的一个恒定比例。此外，COAD 可以使用各种现代机器学习方法，包括随机森林、核方法和深度神经网络，预测竞标者的价值，并确保在任何有限的历史数据样本下有良好的收益表现。此外，COAD 基于竞标者的估值下限引入了竞标者特定的保留价格，不同于文献中常用的统一保留价格。我们通过广泛的模拟和真实数据应用验证了我们的理论预测。所有使用 COAD 和重现结果的代码都可以在 GitHub 上获得。