Debiasing Averaged Stochastic Gradient Descent to handle missing values
Authors: Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the convergence behavior and the relevance of the algorithm not only on synthetic data but also on real data sets, including those collected from medical register. and 5 Experiments, 5.1 Synthetic data, 5.2 Real dataset 1: Traumabase dataset, 5.3 Real dataset 2: Superconductivity dataset |
| Researcher Affiliation | Academia | Aude Sportisse Sorbonne University Paris, France, Claire Boyer Sorbonne University Paris, France, Aymeric Dieuleveut Ecole Polytechnique Palaiseau, France, Julie Josse INRIA Montpellier, France |
| Pseudocode | Yes | Algorithm 1 Averaged SGD for Heterogeneous Missing Data |
| Open Source Code | Yes | The code to reproduce all the simulations and numerical experiments is available on https://github.com/Aude Sportisse/SGD-NA. |
| Open Datasets | No | The paper mentions 'Traumabase dataset' and 'Superconductivity dataset (available here)' but does not provide a direct URL, DOI, or specific citation with authors and year for public access within the provided text. |
| Dataset Splits | No | The dataset is divided into training and test sets (random selection of 70 30%). No explicit mention of a validation set split. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'sklearn.impute.IterativeImputer' but does not provide a specific version number, nor does it list other software dependencies with their versions. |
| Experiment Setup | Yes | Av SGD described in Algorithm 1 with a constant step size α = 1 2L, and L given in (6). and The regularization parameter λ (see Remark 1) is chosen by cross validation. |