主动学习的反馈编码
Feedback Coding for Active Learning
摘要：
主动机器学习中用于标记的示例的迭代选择在概念上类似于信息论中的反馈信道编码：在这两个任务中，目标都是在存在噪声的情况下寻求最小的动作序列来编码信息。虽然之前已经注意到这种高级别的重叠，但如何最好地将主动学习作为一种通信系统来利用反馈编码中的现有分析和算法，仍然存在一些悬而未决的问题。在这项工作中，我们正式确定和利用这两个问题之间的结构共性，包括编码器和噪声通道组件的特性，设计一个新的算法。具体而言，我们开发了一个最佳的基于运输的反馈编码方案称为近似后验匹配（APM）的任务，积极的例子选择和探索其应用贝叶斯逻辑斯谛回归，一个流行的模型在主动学习。我们在各种数据集上评估了APM，并以更低的计算成本展示了与现有主动学习方法相当的学习性能。这些结果表明，直接部署的反馈信道编码的概念，设计有效的主动学习策略的潜力。
英文摘要：
The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise. While this high-level overlap has been previously noted, there remain open questions on how to best formulate active learning as a communications system to leverage existing analysis and algorithms in feedback coding. In this work, we formally identify and leverage the structural commonalities between the two problems, including the characterization of encoder and noisy channel components, to design a new algorithm. Specifically, we develop an optimal transport-based feedback coding scheme called Approximate Posterior Matching (APM) for the task of active example selection and explore its application to Bayesian logistic regression, a popular model in active learning. We evaluate APM on a variety of datasets and demonstrate learning performance comparable to existing active learning methods, at a reduced computational cost. These results demonstrate the potential of directly deploying concepts from feedback channel coding to design efficient active learning strategies.
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