15 citations · 27 across the 5 of their papers we have counts for
5 papers
Online Agnostic Boosting via Regret Minimization
Nataly Brukhim, Xinyi Chen, Elad Hazan +1
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules. While in statistical learning numerous boosting methods exist both in the…
Limits of Private Learning with Access to Public Data
Noga Alon, Raef Bassily, Shay Moran
We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential p…
Private Center Points and Learning of Halfspaces
Amos Beimel, Shay Moran, Kobbi Nissim +1
We present a private learner for halfspaces over an arbitrary finite domain with sample complexity . The building block for t…
Learning to Screen
Alon Cohen, Avinatan Hassidim, Haim Kaplan +2
Imagine a large firm with multiple departments that plans a large recruitment. Candidates arrive one-by-one, and for each candidate the firm decides, based on her data (CV, skills,…
On statistical learning via the lens of compression
Ofir David, Shay Moran, Amir Yehudayoff
This work continues the study of the relationship between sample compression schemes and statistical learning, which has been mostly investigated within the framework of binary cla…