On Locality of Local Explanation Models
Authors: Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C Holmes
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present comprehensive experiments on several standardised real-world tabular UCI data sets [5] of different sizes predicted with ensemble classifiers or regressors, as well as an image classification task on the MNIST dataset. |
| Researcher Affiliation | Collaboration | Sahra Ghalebikesabi University of Oxford sahra.ghalebikesabi@stats.ox.ac.uk Lucile Ter-Minassian University of Oxford lucile.ter-minassian@stats.ox.ac.uk Karla Diaz-Ordaz The London School of Hygiene & Tropical Medicine & The Alan Turing Institute Chris Holmes University of Oxford & The Alan Turing Institute |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We present comprehensive experiments on several standardised real-world tabular UCI data sets [5] of different sizes predicted with ensemble classifiers or regressors, as well as an image classification task on the MNIST dataset. |
| Dataset Splits | No | The paper mentions using several datasets but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using ensemble classifiers (Random Forest, Light GBM, XGBoost) and convolutional neural networks, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper states: 'We present a subset of our results in this Section and refer the interested reader to Supplement K for a thorough report of all experimental results (including simulated experiments), details and hyper-parameter settings.' This indicates that detailed experimental setup, including hyperparameters, is deferred to the supplementary material and not present in the main text. |