Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

Abstract Domain generalization (DG) is essentially an out-of-distribution problem, aiming to
generalize the knowledge learned from multiple source domains to an unseen target …

As various post hoc explanation methods are increasingly being leveraged to explain
complex models in high-stakes settings, it becomes critical to develop a deeper …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …

Leading graph contrastive learning (GCL) methods perform graph augmentations in two
fashions:(1) randomly corrupting the anchor graph, which could cause the loss of semantic …