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..
Inferning with High Girth Graphical Models
Authors: Uri Heinemann, Amir Globerson
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on synthetic data show that the models we learn indeed outperform those obtained by other algorithms, which do not return high girth graphs. |
| Researcher Affiliation | Academia | Uri Heinemann EMAIL The Hebrew University of Jerusalem, Jerusalem, Israel Amir Globerson EMAIL The Hebrew University of Jerusalem, Jerusalem, Israel |
| Pseudocode | Yes | Algorithm 1 Extended Chow Liu |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper uses 'synthetic data' that was generated by the authors, with details on how it was generated ('starting with a random tree structure and then adding random edges', 'parameters hi were drawn from a uniform distribution', 'parameters Jij were drawn from a uniform distribution'), but does not provide access information (link, citation, etc.) to this data. |
| Dataset Splits | No | The paper refers to a 'training sample' and evaluates models based on '100 random queries' from synthetic data, but it does not specify explicit train/validation/test dataset splits with percentages, sample counts, or specific predefined split methodologies. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments, such as GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper describes algorithms like 'loopy belief propagation' and 'Chow Liu algorithm' but does not specify any software dependencies with version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | All the models considered have p = 20 variables, so as to allow exact inference for comparisons. The underlying graphs were constrained to have a girth of g = 8. [...] The ๏ฌeld parameters hi were drawn from a uniform distribution on [ 0.1, 0.1]. The scale of the interaction parameters Jij varied, as described next. [...] The parameters Jij were drawn from a uniform distribution on [ 1.1, 1.1]. [...] the number of samples is always n = 3200. |