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..
Scaling-up Importance Sampling for Markov Logic Networks
Authors: Deepak Venugopal, Vibhav G Gogate
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several MLNs clearly demonstrate the promise of our approach. |
| Researcher Affiliation | Academia | Deepak Venugopal Department of Computer Science University of Texas at Dallas EMAIL Vibhav Gogate Department of Computer Science University of Texas at Dallas EMAIL |
| Pseudocode | Yes | Algorithm 1: Compute-Marginals |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It references the Alchemy system, which is a third-party tool. |
| Open Datasets | Yes | Our test MLNs include Smokers and HMM (with few states) from the Alchemy website [10] and two additional MLNs, Relation (R(x, y) S(y, z)), Log Req (randomly generated formulas with singletons). [10] S. Kok, M. Sumner, M. Richardson, P. Singla, H. Poon, D. Lowd, J. Wang, and P. Domingos. The Alchemy System for Statistical Relational AI. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2008. http://alchemy.cs.washington.edu. |
| Dataset Splits | No | The paper mentions random setting of groundings as true or false (25% each) for MLNs, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts required for reproduction. |
| Hardware Specification | Yes | We ran all experiments on a quad-core, 6GB RAM, Ubuntu laptop. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For clustering, we used the scheme in [19] with KMeans++ as the clustering method. For Gibbs sampling, we set the thinning parameter to 5 and use a burn-in of 50 samples. |