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..
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
Authors: Gersende Fort, Eric Moulines, Hoi-To Wai
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results support our findings. |
| Researcher Affiliation | Academia | Gersende Fort Institut de Math ematiques de Toulouse Universit e de Toulouse; CNRS UPS, Toulouse, France EMAIL Eric Moulines Centre de Math ematiques Appliqu ees Ecole Polytechnique, France CS Dpt, HSE University, Russian Federation EMAIL Hoi-To Wai Department of SEEM The Chinese University of Hong Kong Shatin, Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1: The SPIDER-EM algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | MNIST Dataset. We perform experiment on the MNIST dataset to illustrate the effectiveness of SPIDER-EM on real data; this example is taken from [23, Section 5]. |
| Dataset Splits | No | The paper uses the MNIST dataset but does not specify training, validation, or test splits. It mentions preprocessing steps and estimating a GMM model. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For SPIDER-EM, we set b = dpn/20e, kin = dn/be and a fixed step size γk = 0.01. The minibatch size is set to be b = 100 and the stepsize γ = 5 10 3 except for i EM where γ = 1. The same initial value b Sinit is used for all experiments. We have implemented the procedure of [19] in order to obtain the initialization init and then we set b Sinit def = s( init) ( W(b Sinit) = 58.3). |