Not All Learnable Distribution Classes are Privately Learnable. We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable under (\varepsilon, \delta)-differential privacy. This refutes a conjecture of Ashtiani.
Feb 1, 2024
Mar 15, 2024 · Abstract. We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not ...
Feb 1, 2024 · We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable ...
Given samples from a distribution P belonging to some class of distributions E, can we output a distribution P1 that is close to P in total variation distance?
Nov 12, 2024 · Abstract: We give an example of a class of distributions that is learnable in total variation distance with a finite number of samples, but not learnable ...
Not All Learnable Distribution Classes are Privately Learnable. Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal. February 2024.
We consider the problem of online classification under a privacy constraint. In this setting a learner observes sequentially a stream of labelled examples ...
Jan 28, 2022 · In particular, it does not depend on |H|. Theorem 3 (Littlestone Classes are Privately Learnable). Let H ⊆ {±1}X be a class with Lit- tlestone ...
For any class H, under the realizability assumption, if there is a (0.1, 0.1, 0.1)-pure private learner for H, then H is privately learnable by a pure private ...
Aug 16, 2022 · The converse direction—that every DP-learnable class has a finite Littlestone dimension—utilizes an intimate relationship between thresholds and ...