3 papers
math.ST2026
Estimating eigenvectors and eigenspaces of covariance matrices: Optimal Bounds and Conditions for Consistency
Phuc Tran, Van Vu
Let be a zero-mean random vector of large dimension () with (hidden) covariance matrix $M = (m_{ij})_{1 \leq i, j…
cs.LG2025
Spectral Perturbation Bounds for Low-Rank Approximation with Applications to Privacy
Phuc Tran, Nisheeth K. Vishnoi, Van H. Vu
A central challenge in machine learning is to understand how noise or measurement errors affect low-rank approximations, particularly in the spectral norm. This question is especia…
cs.LG2025
Perturbation Bounds for Low-Rank Inverse Approximations under Noise
Phuc Tran, Nisheeth K. Vishnoi
Low-rank pseudoinverses are widely used to approximate matrix inverses in scalable machine learning, optimization, and scientific computing. However, real-world matrices are often…