Feature Adaptation for Sparse Linear Regression
Authors: Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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, dℓ= 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, dℓ= 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, dℓ= 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. |