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..
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Authors: Dominik Linzner, Michael Schmidt, Heinz Koeppl
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method on synthetic and two real-world data sets. For all experiments, we consider a fixed set of hyper-parameters. |
| Researcher Affiliation | Academia | Dominik Linzner1 Michael Schmidt1 Heinz Koeppl1,2 1Department of Electrical Engineering and Information Technology 2Department of Biology Technische Universität Darmstadt EMAIL |
| Pseudocode | Yes | Algorithm 1 Stationary points of Euler Lagrange equation |
| Open Source Code | Yes | An implementation of our method is available via Git1. 1https://git.rwth-aachen.de/bcs/ssl-ctbn |
| Open Datasets | Yes | We applied our method to the British Household Panel Survey (ESRC Research Centre on Micro-social Change, 2003). |
| Dataset Splits | No | The paper describes how data was used (e.g., 'a varying number of trajectories', 'picked 600 at random') but does not specify explicit train/validation/test dataset splits with percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'standard Matlab implementation of the interior-point method' but does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For all experiments, we consider a fixed set of hyper-parameters. We set the Dirichlet concentration parameter ci = 0.9 for all i {1, . . . , N}. Further, we assume a prior for the generators, which is uninformative on the structure αi(x, x | u) = 5 and βi(x | u) = 10, for all x, x Xi, u Ui. |