Recent Advances in OOD Detection: Problems and Approaches
S Lu, Y Wang, L Sheng, A Zheng, L He… - arXiv preprint arXiv …, 2024 - arxiv.org
Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …
space, which is an essential component in building reliable machine learning systems …
Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark
To build safe and reliable graph machine learning systems, unsupervised graph-level
anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection …
anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection …
Unraveling privacy risks of individual fairness in graph neural networks
Graph neural networks (GNNs) have gained significant attraction due to their expansive real-
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Distribution shifts on graphs--the discrepancies in data distribution between training and
employing a graph machine learning model--are ubiquitous and often unavoidable in real …
employing a graph machine learning model--are ubiquitous and often unavoidable in real …