Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Lifted Hybrid Variational Inference
Authors: Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,285 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,151 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the performance of the proposed lifted variational inference approach on a variety of both real and synthetic models. We compare the performance of our variational approach using different entropy approximations...with message-passing algorithms... |
| Researcher Affiliation | Academia | Yuqiao Chen 1 , Yibo Yang 2 , Sriraam Natarajan1 and Nicholas Ruozzi1 1 The University of Texas at Dallas 2 University of California Irvine |
| Pseudocode | Yes | Algorithm 1 Coarse-to-Fine Lifted VI |
| Open Source Code | Yes | Source code is available on https://github.com/leodd/Lifted-Hybrid-Variational-Inference. |
| Open Datasets | Yes | We construct a Toy Hybrid MLN for a position domain: The Paper Popularity HMLN domain is determined by the following formulas. The Robot Mapping HMLN domain contains 3 discrete relational variables... We performed approximate inference on a RGM with the recession domain from [Cseke and Heskes, 2011]. We use extracted groundwater level data from the Republican River Compact Association model [Mc Kusick, 2003]. |
| Dataset Splits | No | The paper describes generating random evidence for models and randomly choosing variables for testing, but it does not specify any explicit train/validation/test dataset splits, percentages, or cross-validation methods. |
| Hardware Specification | Yes | All timing results, unless otherwise noted, were performed on a single core of a 2.2 GHz Intel Core i7-8750H CPU with 16GB memory. |
| Software Dependencies | No | The paper mentions the use of 'Tensorflow' but does not specify its version number or the versions of any other software dependencies, making replication difficult. |
| Experiment Setup | Yes | All three methods used the Adam optimizer with learning rate 0.2, β1 = 0.9, β2 = 0.999. |