论文标题
动力约束
Power Constrained Bandits
论文作者
论文摘要
上下文匪徒通常在决策问题中提供简单有效的个性化，使其成为流行的工具，以提供在移动健康以及其他健康应用方面的个性化干预措施。但是，当土匪在科学研究的背景下部署时，例如一项临床试验，以测试移动健康干预是否有效 - 目的不仅是为个人个性化，而且还可以通过足够的统计能力确定系统干预是否有效。在更广泛的部署之前，必须评估干预措施的有效性以进行更好的资源分配。这两个目标通常在不同的模型假设下部署，因此很难确定实现个性化和统计能力如何相互影响。在这项工作中，我们开发了一般的元算象来修改现有算法，以确保足够的功率，同时仍可以改善每个用户的福祉。我们还证明，我们的荟萃分析对于可能出现在统计研究中的各种模型错误特异性方面是可靠的，因此为研究设计师提供了有价值的工具。
Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health as well as other health applications. However, when bandits are deployed in the context of a scientific study -- e.g. a clinical trial to test if a mobile health intervention is effective -- the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. It is essential to assess the effectiveness of the intervention before broader deployment for better resource allocation. The two objectives are often deployed under different model assumptions, making it hard to determine how achieving the personalization and statistical power affect each other. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed while still improving each user's well-being. We also demonstrate that our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies, thus providing a valuable tool to study designers.
