4 papers
Missing Mass for Differentially Private Domain Discovery
Travis Dick, Matthew Joseph, Vinod Raman
We study several problems in differentially private domain discovery, where each user holds a subset of items from a shared but unknown domain, and the goal is to output an informa…
Optimal Learning from Label Proportions with General Loss Functions
Lorne Applebaum, Travis Dick, Claudio Gentile +2
Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to…
Nearly Optimal Sample Complexity for Learning with Label Proportions
Robert Busa-Fekete, Travis Dick, Claudio Gentile +3
We investigate Learning from Label Proportions (LLP), a partial information setting where examples in a training set are grouped into bags, and only aggregate label values in each…
Auditing Privacy Mechanisms via Label Inference Attacks
Róbert István Busa-Fekete, Travis Dick, Claudio Gentile +3
We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer t…