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..
Feature Adaptation for Sparse Linear Regression
Authors: Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1 we show that Adapted BP() significantly outperforms standard Basis Pursuit (i.e. Lasso for noiseless data [7]) on a simple example with n = 1000 variables, dā= 10 sparse approximate dependencies, and a ground truth regressor with sparsity t = 13. The simulations were done using Python 3.9 and the Gurobi library [17]. Each figure took several minutes to generate using a standard laptop. |
| Researcher Affiliation | Academia | Jonathan A. Kelner MIT Frederic Koehler Stanford Raghu Meka UCLA Dhruv Rohatgi MIT |
| Pseudocode | Yes | Algorithm 1: Adapted BP for sparse linear regression with few outlier eigenvalues. Algorithm 2: Solve sparse linear regression when covariate eigenspectrum has few outliers |
| Open Source Code | Yes | See the file auglasso.py for code and execution instructions. See Appendix I for implementation details. |
| Open Datasets | No | In Figure 1 we show that Adapted BP() significantly outperforms standard Basis Pursuit ... on a simple example with n = 1000 variables, dā= 10 sparse approximate dependencies, and a ground truth regressor with sparsity t = 13. The covariates X1:1000 are all independent N(0, 1) except for 10 disjoint triplets... The dataset is synthetic and no public access information is provided. |
| Dataset Splits | No | The paper mentions using 'samples' and 'out-of-sample prediction error' but does not specify exact training/validation/test split percentages, absolute counts, or reference predefined splits. |
| Hardware Specification | No | Each figure took several minutes to generate using a standard laptop. This does not provide specific hardware models. |
| Software Dependencies | Yes | The simulations were done using Python 3.9 and the Gurobi library [17]. |
| Experiment Setup | Yes | In Figure 1 we show that Adapted BP() significantly outperforms standard Basis Pursuit ... on a simple example with n = 1000 variables, dā= 10 sparse approximate dependencies, and a ground truth regressor with sparsity t = 13. The covariates X1:1000 are all independent N(0, 1) except for 10 disjoint triplets... The (noiseless) responses are y = 6.25(X1 X2) + 2.5X3 + 1/10 P1000 i=991 Xi. |