Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range.

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.

These kinds of attacks are known as poisoning attacks .