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..
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Authors: Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate in Section 4 the efficacy of our method both in terms of estimation and imputation quality on simulated and survey data examples. We show on simulated and survey data that the method has a clear advantage over current practices. |
| Researcher Affiliation | Academia | Geneviève Robin Centre de Mathématiques Appliquées École Polytechnique, XPOP, INRIA [...] Hoi-To Wai Department of SE&EM The Chinese University of Hong Kong [...] Julie Josse Centre de Mathématiques Appliquées École Polytechnique, XPOP, INRIA [...] Olga Klopp ESSEC Business School CREST, ENSAE [...] Éric Moulines Centre de Mathématiques Appliquées École Polytechnique, XPOP, INRIA |
| Pseudocode | Yes | Algorithm 1 MCGD Method for (9). |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We examine a survey conducted by the French National Institute of Statistics (Insee: http://www.insee.fr/) concerning the hobbies of French people. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies (e.g., library or solver names with version numbers) used for its implementation. |
| Experiment Setup | No | The paper mentions setting regularization parameters λS and λL to theoretical values and a convergence precision of 10^-5, but does not provide specific experimental setup details such as learning rates, batch sizes, or optimizer settings for the proposed method. |