Semi-supervised Batch Active Learning via Bilevel Optimization
This addresses labeling efficiency for machine learning practitioners in semi-supervised active learning, though it appears incremental as it builds on existing batch active learning methods.
The paper tackles the problem of reducing labeling costs in active learning by proposing a novel batch acquisition strategy for semi-supervised settings, formulating it as a bilevel optimization problem for data summarization, and shows it is highly effective in keyword detection tasks with few labeled samples.
Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.