Stepdown SLOPE for Controlled Feature Selection
Authors: Jingxuan Liang, Xuelin Zhang, Hong Chen, Weifu Li, Xin Tang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on simulated data validate the effectiveness of our approaches on controlled feature selection and support our theoretical findings. |
| Researcher Affiliation | Collaboration | Jingxuan Liang1, Xuelin Zhang 2, Hong Chen1, 4, 6,*, Weifu Li1, 5, 6, Xin Tang3 1College of Science, Huazhong Agricultural University, Wuhan 430070, China 2 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 3Ping An Property & Casualty Insurance Company, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1: Accelerated proximal gradient algorithm for SLOPE (3) |
| Open Source Code | No | The paper does not provide an unambiguous statement or a direct link to a source-code repository for the methodology described. |
| Open Datasets | No | The paper primarily uses simulated data, and does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses simulated data and describes the generation process, but it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and testing needed to reproduce the data partitioning of a fixed dataset. |
| Hardware Specification | Yes | All experiments are implemented in Python on a Macbook Pro with Apple M1 and 16 GB memory. |
| Software Dependencies | No | The paper mentions 'implemented in Python' but does not provide specific ancillary software details like library names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | The number of relevant features t is set to vary within {50, 100, 200, 300, 400, 500} and the nonzero regression coefficients are equal to 3 2 log n. We set the target FDR level α = 0.1 and γ = 0.1 for F-SLOPE, and set k = {5, 10, 15, 20, 25, 30} and α = 0.1 for k-SLOPE. |