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摘要翻译：
我们考虑在延迟反馈（DF学习）下训练一个二值分类器。在DF学习中，我们首先接收负样本；随后，部分样本转为阳性。这个问题在各种现实世界的应用程序中都是可以想象的，比如在线广告，在这些应用程序中，用户的操作发生在第一次点击之后很久。由于反馈延迟，简单地分离正负数据会导致样本选择偏差。一种解决方案是假设在第一次观察样本后有一个较长的时间窗口来减少样本选择偏差。然而，现有的研究报告表明，仅使用基于时间窗假设的全部样本中的一部分会产生次优绩效，而使用所有样本与时间窗假设一起会改善经验绩效。在此基础上，我们提出了一种基于时间窗假设的全样本无偏凸经验风险的方法。我们用一个真实的交通日志数据集给出了实验结果，证明了该方法的有效性。
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英文标题：
《Learning Classifiers under Delayed Feedback with a Time Window
Assumption》
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作者：
Masahiro Kato and Shota Yasui
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最新提交年份：
2020
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
We consider training a binary classifier under delayed feedback (DF Learning). In DF Learning, we first receive negative samples; subsequently, some samples turn 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, simply separating the positive and negative data causes a sample selection bias. One solution is to assume that a long time window after first observing a sample reduces the sample selection bias. However, existing studies report that only using a portion of all samples based on the time window assumption yields suboptimal performance, and the use of all samples along with the time window assumption improves empirical performance. Extending these existing studies, we propose a method with an unbiased and convex empirical risk constructed from the whole samples under the time window assumption. We provide experimental results to demonstrate the effectiveness of the proposed method using a real traffic log dataset.
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PDF链接：
https://arxiv.org/pdf/2009.13092
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