Before revision:  However, a learned Bloom filter may under-utilize the model by not taking full advantage of the output. The learned Bloom filter uses the output score by simply applying a threshold, with elements above the threshold being interpreted as positives, and elements below the threshold subject to further analysis independent of the output score (using a smaller backup Bloom filter to prevent false negatives). While recent work has suggested additional heuristic approaches to take better advantage of the score, the results are only heuristic. Here , we instead frame the problem of optimal model utilization as an optimization problem .
After revision:  However, a learned Bloom filter may under-utilize the model by not taking full advantage of the learned model. Here we show how to frame the problem of optimal model utilization as an optimization problem .