We consider training a binary classifier under delayed feedback (\emph{DF
learning}). For example, in the conversion prediction in online ads, we
initially receive negative samples that clicked the ads but did not buy an
item; subsequently, some samples among them buy an item then change to
positive. In the setting of DF learning, we observe samples over time, then
learn a classifier at some point. We initially receive negative samples;
subsequently, some samples among them change to positive. This problem is
conceivable in various real-world applications such as online advertisements,
where the user action takes place long after the first click. Owing to the
delayed feedback, naive classification of the positive and negative samples
returns a biased classifier. One solution is to use samples that have been
observed for more than a certain time window assuming these samples are
correctly labeled. However, existing studies reported that simply using a
subset of all samples based on the time window assumption does not perform
well, and that using all samples along with the time window assumption improves
empirical performance. We extend these existing studies and propose a method
with the unbiased and convex empirical risk that is constructed from all
samples under the time window assumption. To demonstrate the soundness of the
proposed method, we provide experimental results on a synthetic and open
dataset that is the real traffic log datasets in online advertising.

本研究提出了一种使用时间窗口假设下所有样本构建的无偏和凸实验风险来进行 DF 学习的方法，并在在线广告中进行了验证。