Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Authors: Asish Ghoshal, Jean Honorio
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings through synthetic experiments. Figure 2 shows the results of the structure and parameter recovery experiments. |
| Researcher Affiliation | Academia | Asish Ghoshal and Jean Honorio Department of Computer Science, Purdue University, West Lafayette, IN 47906 |
| Pseudocode | Yes | Algorithm 1 Gaussian Bayesian network structure learning algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | For the high-dimensional gene expression data, we used the Breast Cancer dataset by Lu et al. [34] and the Melanoma dataset by Shubbar et al. [35]. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific CPU/GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components used in the experiments. |
| Experiment Setup | Yes | if the regularization parameter is set according to Lemma 2 |