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..
Parallel Bayesian Network Structure Learning
Authors: Tian Gao, Dennis Wei
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the proposed PSL algorithm with different numbers K of agents and compare to the baseline GGSL (Gao et al., 2017) algorithm on the benchmark ALARM dataset. |
| Researcher Affiliation | Industry | Tian Gao* 1 Dennis Wei* 1 1IBM Research, Yorktown Heights, NY 10598 USA. |
| Pseudocode | Yes | Algorithm 1 Local Learn, Algorithm 2 Parallel BN Structure Learning, Algorithm 3 Parallel Local Learn |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the proposed methodology. |
| Open Datasets | Yes | We test the proposed PSL algorithm... on the benchmark ALARM dataset. |
| Dataset Splits | No | We use 1000 samples from the dataset and perform structure learning with different algorithms. |
| Hardware Specification | Yes | The experiments are conducted on a machine with a 2.3GHz Intel i5-5300U processor. |
| Software Dependencies | No | We use an existing Dynamic Programming algorithm (Silander & Myllymaki, 2006) as the BNStruct Learn subroutine for all experiments. |
| Experiment Setup | No | We use 1000 samples from the dataset and perform structure learning with different algorithms. ... We fix p = 0.7 for target selection function choose Target. |