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 [1].
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
Authors: Junhui Zhang, Jingkai Yan, John Wright
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our new method through experiments on simulated and real datasets. Our simulations corroborate the claim that a universal choice of the regularization parameter yields near optimal performance across a range of noise levels, indicating that the proposed method outperforms the (somewhat loose) bound proved here. |
| Researcher Affiliation | Academia | Junhui Zhang Department of Applied Physics and Applied Math Columbia University New York, NY 10027 EMAIL Jingkai Yan Department of Electrical Engineering Columbia University New York, NY 10027 EMAIL John Wright Department of Electrical Engineering Columbia University New York, NY 10027 EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for PCP |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use the hall dataset in [30]... We use the Dark Raw Video (DRV) dataset in [32] (under MIT License)... |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits (e.g., percentages or absolute counts). |
| Hardware Specification | Yes | We run the experiments on a laptop with 2.3 GHz Dual-Core Intel Core i5 |
| Software Dependencies | No | The paper mentions using 'Matlab' and functions like 'rgb2gray()' and 'imadjustn()', but no specific version numbers are provided for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For the added noise, we choose σ {0, 30, 60, 90, 120}... We take λ = 1/ n1, µroot = p n2/2 and µstable = 1 σ( n1+ n2)... set the maximal iteration of ADMM to be 5000. |