Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

Abstract AutoML (Automated Machine Learning) is an emerging field that aims to automate
the process of building machine learning models. AutoML emerged to increase productivity …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

The existing graph neural architecture search (GNAS) methods heavily rely on supervised
labels during the search process, failing to handle ubiquitous scenarios where supervisions …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

Feature transformation aims to generate new pattern-discriminative feature space from
original features to improve downstream machine learning (ML) task performances …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Graph classification is an important problem with applications across many domains, like
chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of …