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..
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods
Authors: Yihan Zhang, Marco Mondelli
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate the significant advantage of our theoretically principled method with the state of the art. |
| Researcher Affiliation | Academia | Yihan Zhang Institute of Science and Technology Austria EMAIL Marco Mondelli Institute of Science and Technology Austria EMAIL |
| Pseudocode | No | The paper describes the AMP algorithm in (5.11) using mathematical equations, but it is not presented as a formal pseudocode block or labeled as an algorithm. |
| Open Source Code | No | All experiments use synthetic data and can be reproduced given the instructions in Section 5. Data and code are not released. |
| Open Datasets | No | The paper uses synthetic data generated with specified parameters (n=4000, d=2000, P=Q=N(0,1)) and does not refer to a publicly available dataset. |
| Dataset Splits | No | The paper uses synthetic data and discusses running '20 i.i.d. trials' for data points, but does not specify training, validation, or test splits in the traditional sense for a dataset. |
| Hardware Specification | No | All experiments are synthetic and can be run efficiently on standard personal computers. No specific hardware details (e.g., CPU/GPU models, memory) are provided. |
| Software Dependencies | No | The paper mentions 'standard SVD algorithms or power iteration' but does not specify any software names with version numbers. |
| Experiment Setup | Yes | In both figures, n = 4000, d = 2000 (so = 2), and P = Q = N(0, 1). Each data point is computed from 20 i.i.d. trials and error bars are reported at 1 standard deviation. We let be either the identity or a Toeplitz matrix [77, 41, 19], i.e., i,j = |i j| with = 0.9. We let be a circulant matrix [40, 39]: the first row has 1 in the first position, c = 0.0078 in the second through (+ 1)-st position and in the last positions (= 300), with the remaining entries being 0; for 2 i d, the i-th row is a cyclic shift of the (i 1)-st row to the right by 1 position. |