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..
Sample based Explanations via Generalized Representers
Authors: Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we conduct empirical comparisons of different generalized representers on two image and two text classification datasets. |
| Researcher Affiliation | Collaboration | Che-Ping Tsai Machine Learning Department Carnegie Mellon University EMAIL Chih-Kuan Yeh Google Deepmind EMAIL Pradeep Ravikumar Machine Learning Department Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks explicitly labeled as such. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For image classification, we follow Pruthi et al. [6] and use MNIST [59] and CIFAR-10 [60] datasets. For text classification, we follow Yeh et al. [53] and use Toxicity4 and AGnews 5 datasets, which contain toxicity comments and news of different categories respectively. 4https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge 5http://groups.di.unipi.it/gulli/AG_corpus_of_news_articles.html |
| Dataset Splits | No | The paper states 'each class containing around 6,000 training samples' and refers to '10 randomly selected testing samples', but does not provide explicit details about training/validation/test splits or a dedicated validation set. |
| Hardware Specification | No | The paper mentions the complexity of the models (e.g., parameter count for CNNs) but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper mentions that 'The CNNs we use for the four datasets comprise 3 layers' and model parameter counts. It refers to 'stochastic gradient descent updates' with 'mini-batch and the learning rate' and mentions Appendix C for more details, but does not provide specific hyperparameter values or detailed system-level training settings in the main text. |