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..
Cone-Constrained Principal Component Analysis
Authors: Yash Deshpande, Andrea Montanari, Emile Richard
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed numerical experiments on synthetic data generated according to the model (1) and with signal v0 = u(n, nε) as defined in the previous section. We provide in the Appendix formulas for the value of limn v0, bv ML , which correspond to continuous black lines in the Figure 1. We compare these predictions with empirical values obtained by running AMP. |
| Researcher Affiliation | Academia | Yash Deshpande Electrical Engineering Stanford University Andrea Montanari Electrical Engineering and Statistics Stanford University Emile Richard Electrical Engineering Stanford University |
| Pseudocode | No | The algorithms are described using mathematical equations (6), (7), and AMP, but are not presented in formal pseudocode or algorithm blocks. |
| Open Source Code | No | No statement or link providing concrete access to source code for the methodology was found. |
| Open Datasets | No | The paper uses 'synthetic data generated according to the model (1)' but provides no access information (link, citation, or repository) for this data. |
| Dataset Splits | No | No specific dataset split information (e.g., percentages, sample counts, or predefined splits with citations) for training, validation, or test sets was found. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or operating systems) were provided. |
| Experiment Setup | Yes | We generated samples of size n = 104, sparsity level ε {0.001, 0.1, 0.8}, and signal-to-noise ratios β {0.05, 0.10, . . . , 1.5}. In each case we run AMP for t = 50 iterations and plot the empirical average of bvt, v0 over 32 instances. |