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..
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
Authors: Borui Wang, Geoffrey Gordon6118-6126
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate its performance on both synthetic and real datasets, and showed that it learns different types of latent graphical models efficiently and achieves superior inference performance. |
| Researcher Affiliation | Academia | Borui Wang Computer Science Department Stanford University EMAIL Geoffrey Gordon Machine Learning Department Carnegie Mellon University EMAIL |
| Pseudocode | Yes | See Appendix A.3 for a pseudocode summary of our algorithm and see Appendix A.5 for the proof of consistency of our algorithm. |
| Open Source Code | No | The paper does not provide a direct link to its source code or explicitly state that the code for their method is open-source or publicly available. |
| Open Datasets | Yes | Pen-Based Recognition of Handwritten Digits dataset in the UCI machine learning repository (Asuncion and Newman 2007). |
| Dataset Splits | Yes | Here we use 7000 samples as our training set, 494 samples as our validation set, and the other 3498 samples as our testing set. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions using 'ridge regression' but does not specify the version numbers of any software libraries or dependencies used (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | In our experiment, we use Gaussian radial basis function kernel embeddings with bandwidth parameter σ = 10 as our feature vectors, and use ridge regression (Friedman, Hastie, and Tibshirani 2009) with regularization parameter λ = 0.1 for S1A and S1B. |