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..
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function
Authors: Yong Liu, Shali Jiang, Shizhong Liao
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approximate cross-validation criterion is sound and efficient. |
| Researcher Affiliation | Academia | Yong Liu EMAIL Shali Jiang EMAIL Shizhong Liao EMAIL School of Computer Science and Technology, Tianjin University, Tianjin 300072, P. R. China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | The evaluation is made on 20 publicly available data sets from LIBSVM Data: 10 data sets for classification and 10 data sets for regression seen in Table 1. |
| Dataset Splits | Yes | For each data set, we have run all the methods 10 times with training and testing data sets be split randomly (50% of all the examples for training and the other 50% for testing). ... For each training set, we choose the τ and λ by cross validation on the training set. |
| Hardware Specification | Yes | Experiments are performed on a Dell Vestro PC with 3.4-GHz 8-core CPU and 8-GB memory. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | We use K(x, x ) = exp( x x 2 2/2τ) as our candidate kernels, τ {2i, i = 6, 5, . . . , 7, 8} 2. The regularization parameter λ {2i, i = 7, 6, . . . , 2}. |