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.
1 code implementation • 31 Jan 2023 • Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression.
no code implementations • 20 Jan 2022 • Russell Impagliazzo, Rex Lei, Toniann Pitassi, Jessica Sorrell
We introduce the notion of a reproducible algorithm in the context of learning.
no code implementations • 14 Jun 2021 • Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos
Our upper and lower bounds characterize the complexity of boosting in the distribution-independent PAC model with Massart noise.
no code implementations • 4 Feb 2020 • Mark Bun, Marco Leandro Carmosino, Jessica Sorrell
To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension.