Not All Learnable Distribution Classes are Privately Learnable
Abstract
We give an example of a class of distributions that is learnable up to constant error in total variation distance with a finite number of samples, but not learnable under (, δ)-differential privacy with the same target error. This weakly refutes a conjecture of Ashtiani.
0
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.