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Jacob Bamberger

5 accepted papers

2026

Carré du champ flow matching: better quality-generalisation tradeoff in generative models

ICLR 2026poster

Deep generative models often face a fundamental tradeoff: high sample quality can come at the cost of memorisation, where the model reproduces training data rather than generalising across the underlying data geometry. We introduce Carré du champ flow matching (CDC-FM), a generalisation of flow matc…

Cited by 0SourceScholar
2026

Riemannian Metric Matching for Scalable Geometric Modeling of Distributions

ICML 2026oral

High-dimensional datasets often concentrate near low-dimensional structures, but estimating their geometry from samples typically relies on graphs and kernels that scale poorly with dataset size and dimension. We propose **Riemannian metric matching**: a denoising probabilistic framework for learnin…

Cited by 0SourceScholar
2025

Bundle Neural Network for message diffusion on graphs

ICLR 2025spotlight

The dominant paradigm for learning on graphs is message passing. Despite being a strong inductive bias, the local message passing mechanism faces challenges such as over-smoothing, over-squashing, and limited expressivity. To address these issues, we introduce Bundle Neural Networks (BuNNs), a novel…

Cited by 1SourcePDFScholar
2025

On Measuring Long-Range Interactions in Graph Neural Networks

ICML 2025poster

Long-range graph tasks --- those dependent on interactions between `distant' nodes --- are an open problem in graph neural network research. Real-world benchmark tasks, especially the Long Range Graph Benchmark, have become popular for validating the long-range capability of proposed architectures.…

Cited by 0SourcePDFScholar
2025

Over-squashing in Spatiotemporal Graph Neural Networks

NeurIPS 2025poster

Graph Neural Networks (GNNs) have achieved remarkable success across various domains. However, recent theoretical advances have identified fundamental limitations in their information propagation capabilities, such as over-squashing, where distant nodes fail to effectively exchange information. Whil…

Cited by 0SourceScholar