AI RESEARCH
Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces
arXiv CS.LG
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ArXi:2508.21466v2 Announce Type: replace In recent years, with the large-scale expansion of graph data, there has been an increased focus on Riemannian manifold data spaces other than Euclidean space. In particular, the development of hyperbolic spaces has been remarkable, and they have high expressive power for graph data with hierarchical structures. Normalized Maximum Likelihood (NML) is employed in regret minimization and model selection.