A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Authors: Ashkan Panahi, Babak Hassibi
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1a depicts the average value w 2 2/n over 50 independent realizations of the LASSO, including independent Gaussian sensing matrices with γ = 0.5, sparse true vectors with κ = 0.2 and Gaussian noise realizations with σ2 = 0.1. We consider two different problem sizes n = 200, 500. |
| Researcher Affiliation | Academia | Ashkan Panahi Department of Electrical and Computer Engineering North Carolina State University Raleigh, NC 27606 apanahi@ncsu.edu Babak Hassibi Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125 hassibi@caltech.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data based on statistical distributions (e.g., 'independent Gaussian sensing matrices', 'sparse true vectors', 'Gaussian noise realizations', 'centered Bernoulli matrix', 'Student s t-distribution', 'asymmetric Bernoulli matrix') rather than using or providing access to a named, publicly available dataset. |
| Dataset Splits | No | The paper describes performing simulations with '50 independent realizations' and '1000 independent realizations' for different problem sizes (n=200, 500) for statistical averaging, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | Yes | Figure 1a depicts the average value w 2 2/n over 50 independent realizations of the LASSO, including independent Gaussian sensing matrices with γ = 0.5, sparse true vectors with κ = 0.2 and Gaussian noise realizations with σ2 = 0.1. We consider two different problem sizes n = 200, 500. |