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 | Conference PDF | Archive PDF | Plain Text | 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. |