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..
Approximate Inference in Logical Credal Networks
Authors: Radu Marinescu, Haifeng Qian, Alexander Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered. |
| Researcher Affiliation | Industry | Radu Marinescu1 , Haifeng Qian2 , Alexander Gray1 , Debarun Bhattacharjya1 , Francisco Barahona1 , Tian Gao1 and Ryan Riegel1 1IBM Research 2AWS AI Labs EMAIL |
| Pseudocode | Yes | Algorithm 1 App Roximate Inf Erence for LCNs (ARIEL) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | Table 3 displays the results obtained on LCNs derived from real-world Bayesian networks with binary variables [Constantinou et al., 2020]. ... Specifically, we consider a binary molecular classification task using imprecise expert knowledge as well as molecular fingerprinting data [Fern andez de Gortari et al., 2017]. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It mentions generating random LCNs and using real-world LCNs but no split details. |
| Hardware Specification | Yes | We ran all experiments on a 2.2GHz Intel Core processor with 32GB of RAM. |
| Software Dependencies | Yes | The competing algorithms were implemented in Python 3.8 and used the ipopt 3.12 solver [W achter and Biegler, 2006] with default settings to handle the non-linear constraint programs. |
| Experiment Setup | No | The paper mentions a maximum of 10 iterations and a 10^-6 threshold for convergence but does not provide other specific hyperparameters or detailed system-level training settings. |