Consider two data providers, each maintaining private records of different feature sets about
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …

How to solve the classification problem with only label proportions has recently drawn
increasing attention in the machine learning field. In this paper, we propose an ensemble …

Most machine learning classifiers, including deep neural networks, are vulnerable to
adversarial examples. Such inputs are typically generated by adding small but purposeful …

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 …

Consider the following data fusion scenario: two datasets/peers contain the same real-world
entities described using partially shared features, eg banking and insurance company …

The Thesis introduces a novel algorithmic framework for weakly supervised learning,
namely, for any any problem in between supervised and unsupervised learning, from the …

It has recently been shown that supervised learning linear classifiers with two of the most
popular losses, the logistic and square loss, is equivalent to optimizing an equivalent loss …

It has recently been shown that supervised learning with the popular logistic loss is
equivalent to optimizing the exponential loss over sufficient statistics about the class …

We describe a system with strong privacy guarantees that is able to learn (supervised) linear
classifiers in the challenging setting where data is distributed, entity matching/resolution is …

Privacy-preserving entity resolution and logistic regression on encrypted data Page 1
Privacy-preserving entity resolution and logistic regression on encrypted data Giorgio Patrini & …