Alexander Sietsema — accepted AI-conference papers

2026

Harmful Overfitting in Sobolev Spaces

Kedar Karhadkar, Alexander Sietsema, Deanna Needell, Guido Montufar

ICML 2026poster

Motivated by recent work on benign overfitting in overparameterized machine learning, we study the generalization behavior of functions in Sobolev spaces $W^{k, p}(\mathbb{R}^d)$ that perfectly fit a noisy training data set. Under assumptions of label noise and sufficient regularity in the data dist…

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2026

VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks

David R Johnson, Alexander Sietsema, Rishabh Anand, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter

ICML 2026poster

We introduce vector diffusion wavelets (VDWs), a novel family of wavelets inspired by the vector diffusion maps algorithm that was introduced to analyze data lying in the tangent bundle of a Riemannian manifold. We show that these wavelets may be effectively incorporated into a family of geometric g…

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