PhD student, Yale University
2 papers at NeurIPS 2025
We derive sharp spectral-norm bounds for low-rank inverse approximations of noisy matrices, improving classical estimates by up to \sqrt{n} and offering spectrum-aware robustness guarantees validated on real and synthetic data.
We derive sharp spectral-norm bounds for noisy low-rank approximation, improving prior results by up to $\sqrt{n}$. Applied to DP-PCA, our method resolves an open problem and matches empirical error via a novel contour bootstrapping technique.
