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..
Enumerate Lasso Solutions for Feature Selection
Authors: Satoshi Hara, Takanori Maehara
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm our theoretical results also in numerical simulations. Finally, in the gene expression and the text data, we demonstrate that the proposed method can enumerate a wide variety of meaningful feature sets, which are overlooked by the global optima. |
| Researcher Affiliation | Academia | Satoshi Hara,1,3 Takanori Maehara2,3 1. National Institute of Informatics, Tokyo, Japan 2. Shizuoka University, Shizuoka, Japan 3. JST, ERATO, Kawarabayashi Large Graph Project |
| Pseudocode | Yes | Algorithm 1 Enumeration algorithm |
| Open Source Code | Yes | 1The experiment codes are available at https://github.com/ sato9hara/Lasso Variants |
| Open Datasets | Yes | We used the thaliana gene expression data used in (Atwell et al. 2010). The 20 Newsgroups 2 is a dataset for text categorization. |
| Dataset Splits | No | The paper states: "we randomly split samples into 134 training samples and 33 test samples." and later "The training set comprised n = 1, 168 samples with p = 11, 648 words, whereas the test set consisted of n = 777 samples." It does not explicitly mention a separate validation split or dataset. |
| Hardware Specification | Yes | All experiments were conducted on 64-bit Cent OS 6.7 with an Intel Xeon E5-2670 2.6GHz CPU and 512GB RAM. |
| Software Dependencies | Yes | All codes were implemented in Python 3.5 with scikit-learn1. |
| Experiment Setup | Yes | For the training set, we applied Algorithm 1 with the regularization parameter ρ = 0.1n and the support parameter η = 0.05. Using Algorithm 1, we enumerated top-50 solutions with the regularization parameter ρ = 0.001n and the support parameter η = 4. |