RKHS-SHAP: Shapley Values for Kernel Methods
Authors: Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |