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..
Uprooting and Rerooting Graphical Models
Authors: Adrian Weller
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide an empirical evaluation in 6, showing that rerooting can be particularly effective for models with dense, strong edges and weak singleton potentials. Section 6. Experiments: We ran experiments on the following topologies and model sizes: complete graphs on 10 and 15 variables; grids of size 5 x 5 and 9 x 9. All potentials were drawn randomly. |
| Researcher Affiliation | Academia | Adrian Weller EMAIL Department of Engineering, University of Cambridge, United Kingdom |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'All methods were implemented using lib DAI (Mooij, 2010)' which is a third-party library, but does not provide access to the authors' own source code for the methodology described in the paper. |
| Open Datasets | No | The paper evaluates inference methods on graphical models with specified topologies (complete graphs, grids) where potentials were drawn randomly. It does not use a fixed, publicly available dataset with a training set in the typical sense. |
| Dataset Splits | No | The paper evaluates inference methods on graphical models with specified topologies and random potentials. It does not refer to traditional train/validation/test splits as it is not a supervised learning task on a fixed dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | Yes | All methods were implemented using lib DAI (Mooij, 2010), see the Appendix 9 for details. |
| Experiment Setup | Yes | All potentials were drawn randomly: mixed models used Wij U[ Wmax, Wmax], attractive models used Wij U[0, Wmax], as Wmax was varied; singleton potentials were drawn either from a low range θi [ 0.1, 0.1], medium range θi [ 2, 2], or from a range commensurate with edge potentials, i.e. θi U[ Wmax/2, Wmax/2], with the factor of 2 needed given the form of (1). |