4 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 2 cited
Boosting in the Presence of Massart Noise
Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane +3
We study the problem of boosting the accuracy of a weak learner in the (distribution-independent) PAC model with Massart noise. In the Massart noise model, the label of each exampl…
cs.LG2020★ 4 cited
Efficient, Noise-Tolerant, and Private Learning via Boosting
Mark Bun, Marco Leandro Carmosino, Jessica Sorrell
We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-…