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..
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Authors: Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse
NeurIPS 2020 | Venue PDF | 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. |