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..
Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level
Authors: Chase Yakaboski, Eugene Santos, Jr
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By leveraging Bayesian Knowledge Bases (BKBs), a framework that operates at the instantiation level and inherently subsumes Bayesian Networks (BNs), we develop both a theoretical MDL score and associated structure learning algorithm that demonstrates significant improvements over learned BNs on 40 benchmark datasets. Further, our algorithm incorporates recent off-the-shelf DAG learning techniques enabling tractable results even on large problems. We then demonstrate the utility of our approach in a significantly under-determined domain by learning gene regulatory networks on breast cancer gene mutational data available from The Cancer Genome Atlas (TCGA). |
| Researcher Affiliation | Academia | Thayer School of Engineering at Dartmouth College, Hanover, NH EMAIL |
| Pseudocode | Yes | Algorithm 1: BKB Structure Learning Input: Dataset D, Source Reliabilities R, DAG learning algorithm f and hyperparameters Θ 1: K 2: for τ D = do 3: Gτ f(τ, R, Θ) 4: K K {Gτ} 5: end for 6: return BKB-Fusion(K, R) |
| Open Source Code | Yes | For source code visit: https://github.com/di2ag/pybkb. |
| Open Datasets | Yes | We then demonstrate the utility of our approach in a significantly under-determined domain by learning gene regulatory networks on breast cancer gene mutational data available from The Cancer Genome Atlas (TCGA). (Tomczak, Czerwińska, and Wiznerowicz 2015) |
| Dataset Splits | Yes | We performed a 10-fold classification cross validation on a subset of only 22 datasets due to the increased learning and reasoning time incurred by running cross validation analysis. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or cloud instance specifications used for running experiments are provided. |
| Software Dependencies | No | The paper mentions using 'GOBNILP' but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper mentions 'hyperparameters Θ' in Algorithm 1 and refers to Appendix A for 'naming conventions and feature selection process', but does not explicitly detail specific hyperparameter values (e.g., learning rate, batch size) or other system-level training settings in the main text. |