Auto-bidding systems are widely used in advertising to automatically determine bid values under constraints such as total budget and Return-on-Spend (RoS) targets. Existing works often assume that the value of an ad impression, such as the conversion rate, is known. This paper considers the more realistic scenario where the true value is unknown. We propose a novel method that uses conformal prediction to quantify the uncertainty of these values based on machine learning methods trained on historical bidding data with contextual features, without assuming the data are i.i.d. This approach is compatible with current industry systems that use machine learning to predict values. Building on prediction intervals, we introduce an adjusted value estimator derived from machine learning predictions, and show that it provides performance guarantees without requiring knowledge of the true value. We apply this method to enhance existing auto-bidding algorithms with budget and RoS constraints, and establish theoretical guarantees for achieving high reward while keeping RoS violations low. Empirical results on both simulated and real-world industrial datasets demonstrate that our approach improves performance while maintaining computational efficiency.

翻译：自动竞价系统在广告领域被广泛应用，用于在总预算和投资回报率（RoS）目标等约束条件下自动确定出价值。现有研究通常假设广告展示的价值（例如转化率）是已知的。本文考虑了一种更现实的场景，即真实价值未知。我们提出了一种新颖的方法，利用保形预测来量化这些价值的不确定性，该方法基于在具有上下文特征的历史竞价数据上训练的机器学习方法，且不假设数据是独立同分布的。此方法与当前使用机器学习预测价值的行业系统兼容。基于预测区间，我们引入了一种从机器学习预测中导出的调整后价值估计器，并证明它能在无需知晓真实价值的情况下提供性能保证。我们将此方法应用于增强现有的具有预算和RoS约束的自动竞价算法，并建立了在保持低RoS违规的同时实现高回报的理论保证。在模拟和真实工业数据集上的实证结果表明，我们的方法在保持计算效率的同时提升了性能。