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 Performance Maximizing Ensembles with Explainability Guarantees
Authors: Vincent Pisztora, Jia Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we describe the data, model training procedures, performance evaluation metrics, and results of our experiments. |
| Researcher Affiliation | Academia | Vincent Pisztora, Jia Li Department of Statistics, Pennsylvania State University, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Following the tabular data benchmarking framework proposed by (Grinsztajn, Oyallon, and Varoquaux 2022), we conduct experiments on a set of 31 datasets (13 classification, 18 regression). |
| Dataset Splits | Yes | Each dataset is split (70%, 9%, 21%) into training, validation, and test sets respectively, following (Grinsztajn, Oyallon, and Varoquaux 2022). |
| Hardware Specification | No | Computations for this research were performed on the Pennsylvania State University s Institute for Computational and Data Sciences Roar supercomputer. This does not provide specific hardware details like GPU/CPU models. |
| Software Dependencies | No | The paper mentions types of models used (e.g., 'logistic regression', 'neural network'), but does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Hyperparameter tuning for all models is done using 4-fold cross-validation, with the exception of the neural network tuning which is done using the validation set. A grid search is done to select the best hyperparameters for each model with search values available in the Appendix of the long form paper available on arxiv.org. |