16 citations · 79 across the 34 of their papers we have counts for
16 papers · 1 filter
Differentially-Private Bayes Consistency
Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich +4
We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to…
Differentially Private Approximate Quantiles
Haim Kaplan, Shachar Schnapp, Uri Stemmer
In this work we study the problem of differentially private (DP) quantiles, in which given dataset and quantiles , we want to output quantile estim…
Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour +1
We give an -differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\s…
Online Markov Decision Processes with Aggregate Bandit Feedback
Alon Cohen, Haim Kaplan, Tomer Koren +1
We study a novel variant of online finite-horizon Markov Decision Processes with adversarially changing loss functions and initially unknown dynamics. In each episode, the learner…
The Sparse Vector Technique, Revisited
Haim Kaplan, Yishay Mansour, Uri Stemmer
We revisit one of the most basic and widely applicable techniques in the literature of differential privacy - the sparse vector technique [Dwork et al., STOC 2009]. This simple alg…
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer +1
We present a differentially private learner for halfspaces over a finite grid in with sample complexity , which improves the…