论文标题
从故事的一面学习真相
Learning the Truth From Only One Side of the Story
论文作者
论文摘要
在单方面的反馈下学习（即，在我们只观察标签以获取积极的示例的标签）是机器学习中的一个基本问题 - 应用程序包括贷款和推荐系统。尽管如此，在减轻产生的采样偏见的影响方面，进展很少。我们专注于广义线性模型，并表明，如果不针对此采样偏差进行调整，该模型可能会次优，甚至无法收敛到最佳解决方案。我们提出了一种自适应方法，该方法具有理论保证，并表明它在经验上优于几种现有方法。我们的方法利用差异估计技术在不确定性下有效地学习，与现有方法相比，提供了更有原则的替代方案。
Learning under one-sided feedback (i.e., where we only observe the labels for examples we predicted positively on) is a fundamental problem in machine learning -- applications include lending and recommendation systems. Despite this, there has been surprisingly little progress made in ways to mitigate the effects of the sampling bias that arises. We focus on generalized linear models and show that without adjusting for this sampling bias, the model may converge suboptimally or even fail to converge to the optimal solution. We propose an adaptive approach that comes with theoretical guarantees and show that it outperforms several existing methods empirically. Our method leverages variance estimation techniques to efficiently learn under uncertainty, offering a more principled alternative compared to existing approaches.
