Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier. However, such learned Bloom filter does not take full advantage of the predicted probability scores. We proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions. We proved our algorithms have lower False Positive Rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter. We also demonstrated the improved performance of our algorithms on real-world datasets.
The paper proposed an adaptive learned bloom filter. Rather than setting a threshold of prediction score, the paper partitions the score into several intervals; for query insider each interval, the paper either uses a group of independent hash functions to hash the query in one unified bloom filter or introduce an independent bloom filter. The paper proposes efficient ways to tune the hyper-parameters, and provides the analysis of the error. Experiments on two applications show the effectiveness of the proposed methods. 
The idea seems useful. However, I have a few concerns/questions. My decision depends on the responses of the author(s). 
(1) Although the paper claims the score information can be fully exploited, the paper seems to do hashing for all possible queries. Why not set up a tau such that only when the score is below tau, we conduct the adaptive hashing? When the score is bigger than tau, we still claim an identification of the key? In this way, a bunch of keys can still be saved and without extra hashing. 
(2) The proposed method seems to be a hierarchical hashing strategy.  The first level is to hash the queries into different intervals through a score function learned from data. Why not compose another group of random hash to do the first level hashing? What is the major benefit of collecting training examples to run a machine learning model? Accordingly, why not compare with such a baseline using a group of random hashing to do the first level?