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..
Latent Dependency Forest Models
Authors: Shanbo Chu, Yong Jiang, Kewei Tu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that LDFMs are competitive with existing probabilistic models. |
| Researcher Affiliation | Academia | Shanbo Chu and Yong Jiang and Kewei Tu School of Information Science and Technology Shanghai Tech University, Shanghai, China EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their proposed method (LDFM). |
| Open Datasets | Yes | We picked nine BNs that are frequently used in the BN learning literature from bnlearn (http://www.bnlearn.com/bnrepository/), a popular BN repository. |
| Dataset Splits | Yes | For each BN, we sampled two training sets of 5000 and 500 instances, one validation set of 1000 instances, and one testing set of 1000 instances. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Libra toolkit' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | No | The paper mentions tuning hyperparameters for other models and using EM for LDFM, but it does not provide concrete hyperparameter values or detailed training configurations. |