Approximate Inference in Logical Credal Networks
Authors: Radu Marinescu, Haifeng Qian, Alexander Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 radu.marinescu@ie.ibm.com |
| 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. |