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..
RKHS-SHAP: Shapley Values for Kernel Methods
Authors: Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate specific properties of RKHS-SHAP and Shapley regularisers using four synthetic experiments, because these properties are best illustrated under a fully controlled environment. |
| Researcher Affiliation | Collaboration | Siu Lun Chau Department of Statistics University of Oxford Robert Hu Amazon London Javier Gonzalez Microsoft Research Cambridge Dino Sejdinovic School of Computer and Mathematical Sciences University of Adelaide |
| Pseudocode | No | The paper describes algorithms and mathematical formulations, but it does not include a distinct pseudocode block or algorithm listing. |
| Open Source Code | Yes | All code and implementations are made publicly available [3]. |
| Open Datasets | Yes | We consider the following 2d Banana distribution B(b 1, v) from Sejdinovic et al. [34]: Sample Z N(0, diag(v, 1)) and transform the data by setting X1 = Z1 and X2 = b 1(Z2 1 v) + Z2. Regression labels are obtained from ftruth(X) = b 1(X2 1 v) + X2. |
| Dataset Splits | Yes | We use 70% of our data for training and 30% for testing. Lengthscales of the kernel are selected using median heuristic [12] and regularisation parameters are selected using cross-validation. |
| Hardware Specification | No | The paper does not specify the exact GPU/CPU models, memory, or other detailed hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using the 'Python package shap [23]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Lengthscales of the kernel are selected using median heuristic [12] and regularisation parameters are selected using cross-validation. Further implementation details and real world data illustrations are included in Appendix E. |