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..
State Variable Effects in Graphical Event Models
Authors: Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on simulated and real-world benchmark datasets, two specific models from the proposed framework are shown to outperform various baselines, highlighting the promising modeling power of the high-level framework. We test the two parametric SVGEMs by evaluating how well they fit simulated and real-world joint temporal datasets. |
| Researcher Affiliation | Industry | Debarun Bhattacharjya , Dharmashankar Subramanian and Tian Gao Research AI, IBM T. J. Watson Research Center EMAIL |
| Pseudocode | No | The paper describes the model formulation and learning process in text, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any specific links to open-source code repositories or explicitly state that the code for the described methodology is available. |
| Open Datasets | Yes | We consider datasets that have been simulated or processed from publicly available sources; experimental details around processing and hyper-parameter choices are omitted here due to space restrictions but will be available on the ar Xiv version. [Frank and Asuncion, 2010] A. Frank and A. Asuncion. UCI machine learning repository, 2010. [Leskovec and Krevl, 2014] J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014. |
| Dataset Splits | Yes | We split each dataset three-ways by event stream into train (70%), dev (15%) and test (15%) sets, optimize each model s hyper-parameters from a grid using the train/dev sets, and then learn the final model on the train set. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'MATLAB simulator' for data generation but does not specify its version or any other software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper states that hyper-parameter choices are 'omitted here due to space restrictions' and 'will be available on the ar Xiv version', meaning specific values or ranges are not provided in the main text. |