Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Singular Subspace Perturbation Bounds via Rectangular Random Matrix Diffusions
Authors: Peiyao Lai, Oren Mangoubi
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present numerical simulations that illustrate the theoretical results in Theorem 2.2, and investigate the extent to which the bounds in Theorem 2.2 are tight. |
| Researcher Affiliation | Academia | Peiyao Lai Worcester Polytechnic Institute Worcester, MA, USA Oren Mangoubi Worcester Polytechnic Institute Worcester, MA, USA |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to code repositories. |
| Open Datasets | No | In this set of simulations, we compute the squared Frobenius error for the rank-k covariance approximation problem... In the following simulations we choose the input data matrix to be a synthetic data matrix with linearly decaying spectral profile spectral profile σi = m (d i + 1) for all i [d]. |
| Dataset Splits | No | The paper uses a synthetic data matrix for numerical simulations, but it does not specify any training, testing, or validation dataset splits. |
| Hardware Specification | No | The paper includes a 'Numerical Simulations' section, but it does not specify any hardware details (e.g., GPU/CPU models, memory) used for running these simulations. |
| Software Dependencies | No | The paper describes numerical simulations but does not provide any specific software dependencies or version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | In this set of simulations, we compute the squared Frobenius error for the rank-k covariance approximation problem... We take an input data matrix A, perturb the matrix by iid Gaussian noise (that is, A = A + TG where G has iid N(0, 1) entries), and compute the error for different values of m, d, k. In the following simulations we choose the input data matrix to be a synthetic data matrix with linearly decaying spectral profile spectral profile σi = m (d i + 1) for all i [d]... Here, d = 15, k = 5, T = 1 and the input matrix has spectral profile σi = m (d i + 1) for all i [d]. |