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 Bayesian Networks in the Presence of Structural Side Information
Authors: Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash7814-7822
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we evaluate the performance and the scalability of our algorithms in both synthetic and real-world structures and show that they outperform the state-of-the-art structure learning algorithms. |
| Researcher Affiliation | Academia | Ehsan Mokhtarian,1 Sina Akbari,1 Fateme Jamshidi,2 Jalal Etesami,1 Negar Kiyavash 1,2 1 Department of Computer and Communication Science, EPFL, Lausanne, Switzerland 2 College of Management of Technology, EPFL, Lausanne, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1: Recursive Structure Learning (RSL). [...] Algorithm 5: Learns BN without side information. |
| Open Source Code | Yes | The MATLAB implementation of our algorithms is publicly available at https://github.com/Ehsan-Mokhtarian/RSL. |
| Open Datasets | Yes | Figure 3 illustrates the performance of BN learning algorithms on two real-world structures, namely Diabetes (Andreassen et al. 1991) and Andes (Conati et al. 1997) networks, over a range of different sample sizes. |
| Dataset Splits | No | The paper describes generating samples for experiments and refers to a 'finite sample setting', but it does not specify the exact percentages or counts for training, validation, and test splits, nor does it detail a cross-validation setup for its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'The MATLAB implementation of our algorithms' and 'Fisher Z-transformation' but does not provide specific version numbers for MATLAB or other key software dependencies. |
| Experiment Setup | Yes | The samples are generated using a linear model where each variable is a linear combination of its parents plus an exogenous noise variable; the coefficients are chosen uniformly at random from [ 1.5, 1] [1, 1.5], and the noises are generated from N(0, σ2), where σ is selected uniformly at random from [ 1.5]. As for the CI tests, we use Fisher Z-transformation (Fisher 1915) with significance level 0.01 in the algorithms (alternative values did not alter our experimental results) and 2 n2 for Mb discovery (Pellet and Elisseeff 2008). |