Privacy-preserving class ratio estimation

Conference/Journal
ACM
Authors
Arun Shankar Iyer J Saketha Nath Sunita Sarawagi
BibTex
Abstract
Abstract In this paper we present learning models for the class ratio estimation problem, which takes as input an unlabeled set of instances and predicts the proportions of instances in the set belonging to the different classes. This problem has applications in social and commercial data analysis. Existing models for class-ratio estimation however require instance-level supervision. Whereas in domains like politics, and demography, set-level supervision is more common. We present a new method for directly estimating class-ratios ...