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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Experimental Design on a Budget for Sparse Linear Models and Applications
Authors: Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a detailed set of experiments, on benchmarks and a large neuroimaging study, showing that the proposed models are effective in practice. |
| Researcher Affiliation | Academia | Sathya N. Ravi EMAIL Vamsi K. Ithapu EMAIL Sterling C. Johnson EMAIL Vikas Singh EMAIL University of Wisconsin Madison William S. Middleton Memorial Veterans Hospital |
| Pseudocode | Yes | Algorithm 1 Alternating Minimization Algorithm, Algorithm 2 Randomized coordinate descent algorithm for solving (12) |
| Open Source Code | Yes | The code is publicly available at https://github.com/sraviuwmadison/Expdesign_sparse. |
| Open Datasets | Yes | two standard LASSO datasets (prostate, (Tibshirani, 1996) and lars, (Efron et al., 2004)) and Alzheimer s Disease Neuroimaging Initiative (ADNI) (neuro). |
| Dataset Splits | No | The paper refers to 'full model' and 'reduced setup' comparisons, and mentions 'train' and 'test' data in the context of the Alzheimer's study, but does not provide specific train/validation/test split percentages or sample counts for any dataset. |
| Hardware Specification | Yes | A single workstation with 8 cores and 32GB RAM is used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We ran 1000 epochs of EDI, and 50 main iterations of ED-S (with 20 iterations for each of its subproblems). |