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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Authors: Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,633 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,998 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The method is demonstrated on applications in image recovery and parametric bilinear estimation. 5 Numerical Experiments |
| Researcher Affiliation | Academia | Alyson K. Fletcher Dept. Statistics UC Los Angeles Parthe Pandit Dept. ECE UC Los Angeles Sundeep Rangan Dept. ECE NYU Subrata Sarkar Dept. ECE The Ohio State Univ. Philip Schniter Dept. ECE The Ohio State Univ. |
| Pseudocode | Yes | Algorithm 1 Vector AMP (LMMSE form) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper refers to |
| Dataset Splits | No | The paper mentions a |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods used (e.g., |
| Experiment Setup | Yes | Figure 1a compares the LASSOand Dn CNN-based versions of AMP and VAMP for 128 128 image recovery under well-conditioned A and no noise. Here, A = JPHD... The sampling rate was fixed at M/N = 0.2, and the measurements were noiseless... For b1 = 20, L = 11, P = 256, K = 10, i.i.d. N(0, 1) matrix A, and SNR = 40 d B. |