Active Learning with Logged Data
Active Learning with Logged Data
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发表时间：
2018-02
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通讯作者：
Songbai Yan;Kamalika Chaudhuri;T. Javidi
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作者：
Songbai Yan;Kamalika Chaudhuri;T. Javidi
我们考虑使用记录的数据进行积极的学习，其中标有标记的示例以预定的记录策略为条件，目标是在整个人群上学习分类器，而不仅仅是根据记录策略的条件。先前的工作要么解决此问题，要么只有可用的数据可用，要么纯粹是在忽略已记录数据的受控随机实验设置中。在这项工作中，我们结合了两种方法，以提供一种算法，该算法使用已记录的数据来引导和为实验提供信息，从而实现了两全其美。我们的工作灵感来自受控的随机实验与主动学习之间的联系，并修改了现有的基于分歧的主动学习算法以利用记录数据。
We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy. Prior work addresses this problem either when only logged data is available, or purely in a controlled random experimentation setting where the logged data is ignored. In this work, we combine both approaches to provide an algorithm that uses logged data to bootstrap and inform experimentation, thus achieving the best of both worlds. Our work is inspired by a connection between controlled random experimentation and active learning, and modifies existing disagreement-based active learning algorithms to exploit logged data.