The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Objectives: Discussions of fairness in criminal justice risk assessments typically lack
conceptual precision. Rhetoric too often substitutes for careful analysis. In this article, we …

Recently, several quantum machine learning algorithms have been proposed that may offer
quantum speed-ups over their classical counterparts. Most of these algorithms are either …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

The most prevalent notions of fairness in machine learning fix a small collection of pre-
defined groups (such as race or gender), and then ask for approximate parity of some …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

We develop and study multicalibration as a new measure of fairness in machine learning
that aims to mitigate inadvertent or malicious discrimination that is introduced at training time …

Prediction systems are successfully deployed in applications ranging from disease
diagnosis, to predicting credit worthiness, to image recognition. Even when the overall …

Autonomous ships are expected to improve the level of safety and efficiency in future
maritime navigation. Such vessels need perception for two purposes: to perform …

This paper presents Verisig, a hybrid system approach to verifying safety properties of
closed-loop systems using neural networks as controllers. We focus on sigmoid-based …