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Showing papers from University of Fribourg Show all papers

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NeurIPS 2026U Fribourg, MILAU FribourgTechnische Universität MünchenUniversité de Montréal; MilaU BergenGraph neural networks

No Triangulation Without Representation: Generalization in Topological Deep Learning

Extending a manifold triangulation benchmark reveals GNNs and HOMP can saturate it with proper representations, yet existing models fail to generalize beyond combinatorial structure.

Johannes S. Schmidt, Martin Carrasco, Ernst Röell, Guy Wolf and 2 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 2/5
80%Must read
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NeurIPS 2026UC Santa BarbaraU Roma "La Sapienza"Universitat Politècnica de CatalU FribourgAalto University, CGIHypergraphs

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

HOPSE replaces higher-order message passing with Hasse-graph encodings to scale linearly on combinatorial domains while matching or exceeding state-of-the-art performance.

Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck, Martin Carrasco and 6 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 0/5
72%Highly rated
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NeurIPS 2026Helmholtz Munich, TU MunichU FribourgUniversité de Montréal; MilaAIDOS Lab, Department of ComputeGraph neural networks

Diversity Curves for Graph Representation Learning

Diversity curves track structural spread across graph coarsening levels to yield interpretable, size-invariant graph embeddings for clustering, visualization, and comparison.

Katharina Limbeck, Nadja Häusermann, Martin Carrasco, Guy Wolf and 1 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5
72%Highly rated
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NeurIPS 2026U FribourgJohns HopkinsTechnical University of MunichU FerraraCentrale SupélecGraph neural networks

On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

Rademacher complexity bounds unify GNN expressivity and input geometry through equivalence classes, covering numbers, and Wasserstein robustness.

Martin Carrasco, Caio Deberaldini Netto, Ehimare Okoyomon, Aneeqa Mehrab and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 1/5