AI RESEARCH
Uncertainty Quantification on Graph Learning: A Survey
arXiv CS.LG
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ArXi:2404.14642v4 Announce Type: replace Graphical models have nstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this survey, we systematically examine existing works on UQ for graphical models.