2026
Provable Accuracy Collapse of Embedding-Based Representations under Dimensionality Mismatch
ICML 2026spotlight
Embedding-based representations in Euclidean space $\mathbb{R}^d$ are a cornerstone of modern machine learning, where a major goal is to use the \emph{smallest dimension} that faithfully captures data relations. In this work, we prove sharp dimension--accuracy tradeoffs and identify a fundamental in…