Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Kernel Random Matrix-Based Approach for Sparse PCA
Authors: Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet
ICLR 2019 | Venue PDF | LLM Run Details | Input Tokens: 20,836 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,159 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 discusses the practical aspects and provides experimental results. Section 6 concludes the article. |
| Researcher Affiliation | Collaboration | 1CEA List, 2Centrale Supรฉlec, 3GIPSA-Lab University of Grenoble Alpes EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | The PCs ui, for i [4] are the Three Peak , Piece Poly , Step New and Sing signals of (Johnstone & Lu, 2009). |
| Dataset Splits | Yes | The soft-parameters a and ฯ (respectively for our method and CT) are selected by cross-validation using a validation set of size n. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | We use p = 2048, n = 1024. The soft-parameters a and ฯ (respectively for our method and CT) are selected by cross-validation using a validation set of size n. The selected parameters are a = 20 and ฯ = 0.1. |