As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Abstract Gene Expression Programming (GEP) is a popular and established evolutionary
algorithm for automatic generation of computer programs. In recent decades, GEP has …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Abstract Domain adaptation algorithms seek to generalize a model trained in a source
domain to a new target domain. In many practical cases, the source and target distributions …

Adaptive networks are suitable for decentralized inference tasks. Recent works have
intensively studied distributed optimization problems in the case where the nodes have to …

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used
for solving many real-world optimization problems. However, traditional GP can only solve a …

Transfer learning, which leverages knowledge from source domains to enhance learning
ability in a target domain, has been proven effective in various applications. One major …

The diffusion LMS algorithm has been extensively studied in recent years. This efficient
strategy allows to address distributed optimization problems over networks in the case …