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 [1].
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. |