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..
Lifted Message Passing for Hybrid Probabilistic Inference
Authors: Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that our approximate lifting schemes perform comparably to existing state-of-the-art models for Gaussian MLNs, while having the flexibility to be applied to models with arbitrary potential functions. |
| Researcher Affiliation | Academia | Yuqiao Chen , Nicholas Ruozzi and Sriraam Natarajan University of Texas at Dallas EMAIL |
| Pseudocode | Yes | Algorithm 1 Lifted Hybrid EPBP |
| Open Source Code | Yes | EPBP, LEPBP, C2FEPBP, and LGa BP were implemented in Python 3.6, and all source code is available on Git Hub1. 1Code: github.com/leodd/Hybrid-Lifted-Belief-Propagation |
| Open Datasets | Yes | We used groundwater level data extracted from the Republican River Compact Association model [Mc Kusick, 2003], which is a monthly record of the measured head position of 3420 wells over 850 months. |
| Dataset Splits | No | The paper describes experiments on probabilistic inference models using evidence and observations, but it does not specify explicit training or validation dataset splits typically used in supervised learning contexts. |
| Hardware Specification | Yes | All experiments were performed on a machine with a 2.2 GHz Intel Core i7-8750H CPU and 16 GB of memory. |
| Software Dependencies | Yes | EPBP, LEPBP, C2FEPBP, and LGa BP were implemented in Python 3.6 |
| Experiment Setup | Yes | All message-passing algorithms were run for 15 iterations and sampling-based methods used 20 sampling points for the integral approximations. For coarse-to-fine lifting, we use k-means clustering with k = 2 for evidence group splitting and use dynamic splitting of the threshold which was initially being set to ϵ = max Sa S(avg(v Sa)) and was decreased each iteration until ϵ = 0. |