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Pool-based active learning (AL) is a promising technology for increasingdata-efficiency of machine learning models. However, surveys show thatperformance of recent AL methods is very sensitive to the choice of dataset andtraining setting, making them unsuitable for general application. In order totackle this problem, the field Learning Active Learning (LAL) suggests to learnthe active learning strategy itself, allowing it to adapt to the given setting.In this work, we propose a novel LAL method for classification that exploitssymmetry and independence properties of the active learning problem with anAttentive Conditional Neural Process model. Our approach is based on learningfrom a myopic oracle, which gives our model the ability to adapt tonon-standard objectives, such as those that do not equally weight the error onall data points. We experimentally verify that our Neural Process modeloutperforms a variety of baselines in these settings. Finally, our experimentsshow that our model exhibits a tendency towards improved stability to changingdatasets. However, performance is sensitive to choice of classifier and morework is necessary to reduce the performance the gap with the myopic oracle andto improve scalability. We present our work as a proof-of-concept for LAL onnonstandard objectives and hope our analysis and modelling considerationsinspire future LAL work.

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes