My score remains the same. The methods proposed in the paper elegantly deals with the problem of redundant acquisition when using BALD in a greedy manner. I have a few questions and hope the authors can address them: (1) Does this problem of redundant acquisition only happen when one uses BALD as the score? Intuitively I would think no, as if one uses any score function greedily, regardless of the contribution of the other samples selected in the same batch, one can still end up with a biased batch that can potentially harm training. If this is the case, then why are var-ratios and mean-std outperforming random?

The paper proposes BatchBALD, a batch acquisition function for sample selection in active learning. A greedy optimization algorithm is presented for efficient sample selection and BatchBALD score maximization. The reviewers and AC agree that this is an interesting work and that the approach is clearly presented and convincing. In addition the author response satisfactorily addresses the points raised in the reviews.

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time 1 - icefrac{1}{e} -approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time $1 - icefrac{1}{e}$-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition. Papers published at the Neural Information Processing Systems Conference.