Adjusted Mutual Information, are type II biased.

In this paper, we initiate the study of Euclidean clustering with Distance-based privacy. Distance-based privacy is motivated by the fact that it is often only needed to protect the privacy of exact, rather than approximate, locations.

More generally, any finite subset of a hyperbolic space "looks like" a finite tree.

While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's'human-like behavior' tasks), few, if not none, examine creative problem solving abilities.