Multi-class classification problem is among the most popular and well-studied statistical
frameworks. Modern multi-class datasets can be extremely ambiguous and single-output …

We study the problem of fair binary classification using the notion of Equal Opportunity. It
requires the true positive rate to distribute equally across the sensitive groups. Within this …

We propose a novel method for selective classification (SC), a problem which allows a
classifier to abstain from predicting some instances, thus trading off accuracy against …

We study the problem of learning an optimal regression function subject to a fairness
constraint. It requires that, conditionally on the sensitive feature, the distribution of the …

Deep neural networks have shown dramatic improvements in a lot of supervised
classification tasks. Such models are usually trained with the objective to ultimately minimize …

In this paper we introduce a nearest neighbor based estimate of the prédiction interval with
prescribed conditional coverage probability and with small length. In the spécial case, when …

When many labels are possible, choosing a single one can lead to low precision. A common
alternative, referred to as top-$ K $ classification, is to choose some number $ K $(commonly …

We address the problem of performing regression while ensuring demographic parity, even
without access to sensitive attributes during inference. We present a general-purpose post …

Cost-sensitive learning is a common type of machine learning problem where different
errors of prediction incur different costs. In this paper, we design a generic nonparametric …

Abstract Machine learning based systems and products are reaching society at large in
many aspects of everyday life, including financial lending, online advertising, pretrial and …