4 citations · 5 across the 6 of their papers we have counts for
3 papers · 1 filter
Private Boosted Decision Trees via Smooth Re-Weighting
Vahid R. Asadi, Marco L. Carmosino, Mohammadmahdi Jahanara +2
Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantee…
Learning with distributional inverters
Eric Binnendyk, Marco Carmosino, Antonina Kolokolova +2
We generalize the "indirect learning" technique of Furst et. al., 1991 to reduce from learning a concept class over a samplable distribution to learning the same concept class…
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-…