Causal inference is the idea of cause-and-effect; this fundamental area of sciences can be
applied to problem space associated with Newton's laws or the devastating COVID-19 …

The existing graph neural architecture search (GNAS) methods assume that the graph tasks
are static during the search process, ignoring the ubiquitous scenarios where sequential …

In an era marked by the increasing adoption of Large Language Models (LLMs) for various
tasks, there is a growing focus on exploring LLMs' capabilities in handling web data …

As software systems grow increasingly intricate, Artificial Intelligence for IT Operations
(AIOps) methods have been widely used in software system failure management to ensure …

Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving,
decision-making, and understanding the world. While large language models (LLMs) can …

Sequential recommendation, leveraging user-item interaction histories to provide
personalized and timely suggestions, has drawn significant research interest recently. With …

Traditional domain-specific causal discovery relies on expert knowledge to guide the data-
based structure learning process, thereby improving the reliability of recovered causality …

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation
of online service systems by promptly notifying engineers of potential issues. However, the …

Discovering the underlying Directed Acyclic Graph (DAG) from time series observational
data is highly challenging due to the dynamic nature and complex nonlinear interactions …

Data-driven tools for asset health management face significant challenges, including a lack
of understanding of physical principles, difficulty incorporating domain experts' experiences …