10 citations · 22 across the 15 of their papers we have counts for
5 papers · 1 filter
PAC learning with stable and private predictions
Yuval Dagan, Vitaly Feldman
We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of…
Interaction is necessary for distributed learning with privacy or communication constraints
Yuval Dagan, Vitaly Feldman
Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive v…
Learning from weakly dependent data under Dobrushin's condition
Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala +1
Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving t…
Optimality of Maximum Likelihood for Log-Concave Density Estimation and Bounded Convex Regression
Gil Kur, Yuval Dagan, Alexander Rakhlin
In this paper, we study two problems: (1) estimation of a -dimensional log-concave distribution and (2) bounded multivariate convex regression with random design with an underly…
Space lower bounds for linear prediction in the streaming model
Yuval Dagan, Gil Kur, Ohad Shamir
We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natur…