  Active learning is a powerful method for training machine learning models
with limited labeled data. One commonly used technique for active learning is
BatchBALD, which uses Bayesian neural networks to find the most informative
points to label in a pool set. However, BatchBALD can be very slow to compute,
especially for larger datasets. In this paper, we propose a new approximation,
k-BALD, which uses k-wise mutual information terms to approximate BatchBALD,
making it much less expensive to compute. Results on the MNIST dataset show
that k-BALD is significantly faster than BatchBALD while maintaining similar
performance. Additionally, we also propose a dynamic approach for choosing k
based on the quality of the approximation, making it more efficient for larger
datasets.
