从归因集合中进行统计学习
Statistical Learning from Attribution Sets
AI总结：
针对隐私约束下广告点击与转化无法直接关联的问题，提出基于归因集合的无偏损失估计方法，实现经验风险最小化的泛化保证，并优于行业启发式方法。
摘要：
我们解决了隐私约束下广告领域转化预测模型的训练问题，其中点击和转化之间缺乏直接链接。受隐私保护浏览器API和第三方cookie弃用的启发，我们研究了一种设置，其中学习器观察到一系列点击和一系列转化，但只能将转化与一组候选点击（归因集合）相关联，而不是唯一的来源。我们将此形式化为从由具有候选先验分布的无知对手生成的归因集合中进行学习。尽管缺乏显式标签，我们通过一种新颖的方法从这些粗粒度信号中构建了总体损失的无偏估计量。利用该估计量，我们表明经验风险最小化实现了泛化保证，该保证随先验的信息量而缩放，并且对先验的估计误差也具有鲁棒性，尽管归因集合之间存在复杂的依赖关系。在标准数据集上的简单实证评估表明，我们的无偏方法显著优于常见的行业启发式方法，特别是在归因集合较大或重叠的情况下。
英文摘要：
We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. We formalize this as learning from attribution sets generated by an oblivious adversary equipped with a prior distribution over the candidates. Despite the lack of explicit labels, we construct an unbiased estimator of the population loss from these coarse signals via a novel approach. Leveraging this estimator, we show that Empirical Risk Minimization achieves generalization guarantees that scale with the informativeness of the prior and is also robust against estimation errors in the prior, despite complex dependencies among attribution sets. Simple empirical evaluations on standard datasets suggest our unbiased approach significantly outperforms common industry heuristics, particularly in regimes where attribution sets are large or overlapping.
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