Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …

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 …

Automated machine learning (AutoML) has achieved remarkable success in automating the
non-trivial process of designing machine learning models. Among the focal areas of AutoML …

Sequential recommendation aims to recommend the next items that a target user may have
interest in based on the user's sequence of past behaviors, which has become a hot …

Human albumin is essential for indicating the body's overall health. Accurately predicting
plasma albumin levels and determining appropriate doses are urgent clinical challenges …

The increasing size of language models necessitates a thorough analysis across multiple
dimensions to assess trade-offs among crucial hardware metrics such as latency, energy …

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

Graph Neural Architecture Search (GNAS) has achieved superior performance on various
graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS …

Recent prevailing works on graph machine learning typically follow a similar methodology
that involves designing advanced variants of graph neural networks (GNNs) to maintain the …

Multimodal graph neural architecture search (MGNAS) has shown great success for
automatically designing the optimal multimodal graph neural network (MGNN) architecture …