15 citations · 99 across the 45 of their papers we have counts for
8 papers · 2 filters
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…
On Optimal Learning Under Targeted Data Poisoning
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody +2
Consider the task of learning a hypothesis class in the presence of an adversary that can replace up to an fraction of the examples in the training set with arbit…
Fine-Grained Distribution-Dependent Learning Curves
Olivier Bousquet, Steve Hanneke, Shay Moran +2
Learning curves plot the expected error of a learning algorithm as a function of the number of labeled samples it receives from a target distribution. They are widely used as a mea…
Understanding Generalization via Leave-One-Out Conditional Mutual Information
Mahdi Haghifam, Shay Moran, Daniel M. Roy +1
We study the mutual information between (certain summaries of) the output of a learning algorithm and its training data, conditional on a supersample of i.i.d. data from…
A Resilient Distributed Boosting Algorithm
Yuval Filmus, Idan Mehalel, Shay Moran
Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize. We present a…
Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan +1
Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annot…