In recent years, a plethora of approaches have been proposed to deal with the increasingly
challenging task of multi‐output regression. This study provides a survey on state‐of‐the‐art …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Multi-label classification has rapidly attracted interest in the machine learning literature, and
there are now a large number and considerable variety of methods for this type of learning …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

The goal of multilabel (ML) classification is to induce models able to tag objects with the
labels that better describe them. The main baseline for ML classification is binary relevance …

In this paper, we tackle the challenges of multilabel classification by developing a general
conditional dependency network model. The proposed model is a cyclic directed graphical …