One such filter, the partitioned learned Bloom filter (PLBF), achieves excellent memory efficiency.

Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications.

Experiments were run with a 25-fold cross-validation.

These kinds of attacks are known as poisoning attacks .

Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP).