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Hannah Sansford

2 accepted papers

2026

How high is ‘high’? Rethinking the roles of dimensionality in topological data analysis and manifold learning

ICML 2026poster

High-dimensionality of data is often regarded as a fundamental statistical impediment in Machine Learning and AI. The purpose of this paper is to clarify, on the contrary, when and how high-dimensionality may be beneficial. In the setting of a general random function model of data we delineate betwe…

Cited by 0Source ↗Scholar ↗
2023

Implications of sparsity and high triangle density for graph representation learning

AISTATS 2023poster

Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inner products. Here, we show that such graphs can be reproduced using an infinite-dimensional inner product model, where th…

Cited by 1Source ↗PDF ↗Scholar ↗