Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Authors: Asish Ghoshal, Jean Honorio
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,012 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,489 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical ο¬ndings 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 |