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.
AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms
Authors: Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
JAIR 2020 | Venue PDF | LLM Run Details | Input Tokens: 40,224 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,142 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 | Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified versions of) PC-like algorithm. |
| Researcher Affiliation | Academia | Mohammad Ali Javidian EMAIL Marco Valtorta EMAIL Pooyan Jamshidi EMAIL Department of Computer Science and Engineering University of South Carolina, Columbia, SC, 29201, USA. |
| Pseudocode | Yes | Algorithm 1: Test for minimal separation (Problem 1) ... Algorithm 6: LCD-AMP : A decomposition-based recovery algorithm for AMP CGs |
| Open Source Code | Yes | Code for reproducing our results is available at https://github.com/majavid/AMPCGs2019. |
| Open Datasets | Yes | Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP... We perform simulation studies for four well-known Bayesian networks from Bayesian Network Repository (Scutari, 2017): ASIA, INSURANCE, ALARM, and HAILFINDER. |
| Dataset Splits | No | The paper describes generating i.i.d. samples of size n {500, 1000, 5000, 10000} for random AMP CGs and using 'observations' for discrete Bayesian networks. While sample sizes are given, there are no explicit details on training, validation, or testing splits. |
| Hardware Specification | No | The paper mentions parallel computations and distributing tasks over different cores, but it does not specify any particular CPU or GPU models, or other detailed hardware specifications for the experiments. |
| Software Dependencies | No | The paper mentions 'gaussCItest() function from the R package pcalg' and 'bnlearn R package (Scutari, 2017)'. While R packages are named, specific version numbers for these packages or for R itself are not provided. |
| Experiment Setup | Yes | In our simulation, we change three parameters p (the number of vertices), n (sample size) and N (expected number of adjacent vertices) as follows: p {10, 20, 30, 40, 50}, n {500, 1000, 5000, 10000}, and N {2, 3}. For each sample, three different significance levels (α = 0.005, 0.01, 0.05) are used to perform the hypothesis tests. |