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stat.ML2026
Generative Modeling on Metric Graphs via Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod +1
We introduce, to our knowledge, the first deep generative modeling framework for probability distributions continuously supported on compact metric graphs. Given source and target…
stat.ML2026
Entropic Riemannian Neural Optimal Transport
Alessandro Micheli, Silvia Sapora, Anthea Monod +1
Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian manifolds, where Euclidean geomet…
stat.ML2026
Approximating Persistent Homology for Large Datasets
Yueqi Cao, Anthea Monod
Persistent homology is an important methodology in topological data analysis which adapts theory from algebraic topology to data settings. Computing persistent homology produces pe…