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Showing papers from Institute of Statistical Mathematics Show all papers

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NeurIPS 2026Institute of Statistical MathemaOnto Innovation The Institute of Statistical MatInstitute of Statistical MathemaGraph neural networks

Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM

Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026East China NormalEast China nornal universityInstitute of Statistical MathemaFlow matching

Geometric Velocity Regularity for Flow Matching on Manifold-Concentrated Data

Shuntuo Xu, Zhou Yu, Kenji Fukumizu

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Institute of Statistical MathemaRIKEN AIPThe Institute of Statistical MatEast China NormalU ChicagoFlow matching

Flow Matching from Viewpoint of Proximal Operators

Optimal transport conditional flow matching equals exact proximal operators via extended Brenier potentials without density assumptions, yields explicit vector fields, converges with batch size, and contracts exponentially normal to manifold-supported targets.

Kenji Fukumizu, Wei Huang, Han Bao, Shuntuo Xu and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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