论文标题
在高斯流程的监督下积极学习
Active Learning with Weak Supervision for Gaussian Processes
论文作者
论文摘要
注释监督学习的数据可能是昂贵的。当注释预算有限时，可以使用主动学习来选择和注释那些可能在模型性能中获得最大收益的观察结果。我们提出了一种主动学习算法，除了选择要注释的观察外，还选择了获取的注释的精度。假设精确度较低的注释可获得更便宜，这使该模型可以探索输入空间的更大部分，并具有相同的注释预算。我们在先前提出的高斯过程的秃头目标上构建了采集功能，并从经验上证明了能够调整主动学习循环中注释精度的收益。
Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate those observations that are likely to give the most gain in model performance. We propose an active learning algorithm that, in addition to selecting which observation to annotate, selects the precision of the annotation that is acquired. Assuming that annotations with low precision are cheaper to obtain, this allows the model to explore a larger part of the input space, with the same annotation budget. We build our acquisition function on the previously proposed BALD objective for Gaussian Processes, and empirically demonstrate the gains of being able to adjust the annotation precision in the active learning loop.
