Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization

no code implementations • 22 Mar 2023 • Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell

In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems.

PAC learning

Differentially Private Sampling from Distributions

no code implementations • NeurIPS 2021 • Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg

We demonstrate that, in some parameter regimes, private sampling requires asymptotically fewer observations than learning a description of $P$ nonprivately; in other regimes, however, private sampling proves to be as difficult as private learning.

Multiclass versus Binary Differentially Private PAC Learning

no code implementations • NeurIPS 2021 • Mark Bun, Marco Gaboardi, Satchit Sivakumar

We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning.

PAC learning