Andrew S. Lee

collaborators

2 papers

stat.ML2026

Tensor learning with orthogonal, Lorentz, and symplectic symmetries

Wilson G. Gregory, JosuÃ© Tonelli-Cueto, Nicholas F. Marshall +2

Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to pro…

cs.CG2025

$O (k)$ -Equivariant Dimensionality Reduction on Stiefel Manifolds

Andrew Lee, Harlin Lee, Jose A. Perea +2

Many real-world datasets live on high-dimensional Stiefel and Grassmannian manifolds, $V_{k} (R^{N})$ and $G r (k, R^{N})$ respectively, and benefit from projection onto l…

Tensor learning with orthogonal, Lorentz, and symplectic symmetries

O(k)-Equivariant Dimensionality Reduction on Stiefel Manifolds

$O (k)$ -Equivariant Dimensionality Reduction on Stiefel Manifolds