We study the relationship between two desiderata of algorithms in statis...
We investigate the computational efficiency of multitask learning of Boo...
We present two sample-efficient differentially private mean estimators f...
We give a novel, unified derivation of conditional PAC-Bayesian and mutu...
We provide an information-theoretic framework for studying the generaliz...
In this work we present novel differentially private identity
(goodness-...
We present new differentially private algorithms for learning a large-ma...
We study a recent model of collaborative PAC learning where k players wi...