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..
Compiling Bayesian Network Classifiers into Decision Graphs
Authors: Andy Shih, Arthur Choi, Adnan Darwiche7966-7974
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results relating to scalability are then given in Section 6, followed by a case study in Section 7. |
| Researcher Affiliation | Academia | Andy Shih, Arthur Choi, Adnan Darwiche Computer Science Department University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 compile-classifier(B, π) ... Algorithm 2 compile-subclassifier(B, u, π, k) ... Algorithm 3 block-order(B, X) |
| Open Source Code | No | The paper mentions using the 'Sam IAm library' but does not provide access to the authors' own implementation code for their proposed method. |
| Open Datasets | Yes | The win95pts network is used to diagnose why a printing job has failed (Breese and Heckerman 1996). ... Next, we consider the Andes network, which models students problem-solving skills in physics (Gertner, Conati, and Van Lehn 1998). |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | Inference calls were performed using the Sam IAm library.2 ... 2Available at http://reasoning.cs.ucla.edu/samiam/ (No version number mentioned for the library.) |
| Experiment Setup | Yes | We assume a threshold of 1/2. ... The ODD size and compilation time are also significantly affected by the threshold of the classifier. A heavily biased threshold can lead to a very small ODD and a short compilation time, while a balanced threshold generally leads to larger ODDs. |