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..
Regression with reject option and application to kNN
Authors: Ahmed Zaoui, Christophe Denis, Mohamed Hebiri
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, a numerical study is performed to illustrate the benefit of using the proposed procedure. |
| Researcher Affiliation | Academia | Ahmed Zaoui LAMA, Université Gustave Eiffel EMAIL Christophe Denis LAMA, Université Gustave Eiffel MIA-Paris, Agro Paris Tech, INRAE, Université Paris-Saclay EMAIL Mohamed Hebiri LAMA, Université Gustave Eiffel CREST, ENSAE, Institut Polytechnique de Paris EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' sections, nor any structured code-like blocks. |
| Open Source Code | Yes | The code used for the implementation of the plug-in ε-predictor can be found at https://github.com/Zaoui Amed/Neurips2020_ Reject Option. |
| Open Datasets | Yes | The performance is evaluated on two benchmark datasets: QSAR aquatic toxicity and Airfoil Self Noise coming from the UCI database. |
| Dataset Splits | Yes | For all datasets, we split the data into three parts (50 % train labeled, 20 % train unlabeled, 30 % test). |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | For random forests and svm procedures, we used respectively the R packages random Forest and e1071 with default parameters. |
| Experiment Setup | Yes | We employ the 10-fold cross-validation to select the parameter k {5, 10, 15, 20, 30, 50, 70, 100, 150} of the k NN algorithm. |