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..
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
Authors: Xin Guo, Jun Fan, Ding-Xuan Zhou
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some numerical simulations for both artificial and real MHC-peptide binding data involving the ℓq regularizer and the SCAD penalty are presented to demonstrate the sparsity and error analysis. |
| Researcher Affiliation | Academia | Xin Guo EMAIL Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong, China Jun Fan EMAIL Department of Statistics University of Wisconsin-Madison 1300 University Avenue, Madison, WI53706, USA Ding-Xuan Zhou EMAIL Department of Mathematics City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong, China |
| Pseudocode | No | The paper describes methodologies and mathematical derivations in prose and equations but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available, nor does it include any links to code repositories. |
| Open Datasets | Yes | We apply RKPCA to the quantitative Immune Epitope Database (IEDB) benchmark data of human leukocyte antigen (HLA) peptide binding affinities, introduced in (Nielsen et al., 2008). |
| Dataset Splits | Yes | We divide z evenly into five disjoint subsets z = 5 j=1zj, and do 5-fold cross-validation to select the parameter γ from a geometric sequence {10 10, , 10 2} of length 60, to minimize the root-mean-square error (RMSE). |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud platforms with specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or versions (e.g., programming languages, libraries, or frameworks with version numbers) used for implementing the described methods. |
| Experiment Setup | Yes | We divide z evenly into five disjoint subsets z = 5 j=1zj, and do 5-fold cross-validation to select the parameter γ from a geometric sequence {10 10, , 10 2} of length 60, to minimize the root-mean-mean-square error (RMSE). |