Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

Abstract Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy,
whether the results are trustworthy is still unexplored. Previous studies suggest that many …

The interdependence between nodes in graphs is key to improve class prediction on nodes,
utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs) …

Learning on graphs, where instance nodes are inter-connected, has become one of the
central problems for deep learning, as relational structures are pervasive and induce data …

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries
embed a hidden backdoor trigger during the training process for malicious prediction …

Most existing deep learning models are trained based on the closed-world assumption,
where the test data is assumed to be drawn iid from the same distribution as the training …