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..
Optimal Feature Selection for Decision Robustness in Bayesian Networks
Authors: YooJung Choi, Adnan Darwiche, Guy Van den Broeck
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach. |
| Researcher Affiliation | Academia | Yoo Jung Choi, Adnan Darwiche, and Guy Van den Broeck Computer Science Department University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1 SDPd,T (X | Y, e) and Algorithm 2 FS-SDD(Q, d, b) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions a third-party tool, SAMIAM, but not its own code. |
| Open Datasets | Yes | We evaluated our system on Naive Bayes networks from the UCI repository [Bache and Lichman, 2013], BFC (http://www.berkeleyfreeclinic. org/), and CRESST (http://www.cse.ucla.edu/). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | a 2.6GHz Intel Xeon E5-2670 CPU with 4GB RAM was used. |
| Software Dependencies | No | The paper mentions using 'jointree inference as implemented in SAMIAM' but does not provide a specific version number for SAMIAM or any other software dependencies with versions. |
| Experiment Setup | Yes | For each network, we find the optimal subset for E-SDP with the budget set to 1/3 the number of features. In all experiments, the cost of each feature is 1, timeout is 1 hour, and a 2.6GHz Intel Xeon E5-2670 CPU with 4GB RAM was used. |