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..
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Authors: Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform various experiments on real datasets to validate our theoretical results, assessing the existence of these two regimes on classification problems and illustrating our convergence results. |
| Researcher Affiliation | Academia | Valentin De Bortoli University of Oxford EMAIL Alain Durmus Université Paris-Saclay EMAIL Xavier Fontaine Université Paris-Saclay EMAIL Umut Sim sekli LTCI, Télécom Paris, Institut Polytechnique de Paris EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code will be made available. |
| Open Datasets | Yes | We focus on the classification task for two datasets: MNIST [41] and CIFAR-10 [42]. |
| Dataset Splits | No | The paper mentions "training and test accuracies" but does not explicitly specify validation dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | If not specified, we set α = 0, M = 100, T = 100, γ = 1. ... We consider the following set of parameters α = 0, M = 100, T = 10000, γ = 0.1. |