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..
Learning in Reproducing Kernel Kreı̆n Spaces
Authors: Dino Oglic, Thomas Gaertner
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The approach is evaluated empirically using indefinite kernels defined on structured as well as vectorial data. The empirical results demonstrate a superior performance of our approach over the state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Nottingham, UK 2Institut f ur Informatik III, Universit at Bonn, Germany. |
| Pseudocode | No | The paper describes algorithms and derivations mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing its source code or links to a code repository. |
| Open Datasets | Yes | All datasets have been downloaded from the LIBSVM library (Chang & Lin, 2011). ... using a set of benchmark datasets for learning with indefinite kernels (Duin & Pekalska, 2009). |
| Dataset Splits | Yes | We measure the effectiveness of a baseline/method using the average root mean squared error, computed after performing 10 fold outer cross-validation. ... 10 fold stratified cross-validation. |
| Hardware Specification | No | The paper mentions 'University of Nottingham High Performance Computing Facility' in the acknowledgements, but it does not specify any particular hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions 'L-BFGS-B minimization procedure' and 'LIBSVM library (Chang & Lin, 2011)' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In Section 3.3, we derive the gradient of an optimal solution to the risk minimization problem with respect to the hyperparameters of the model (e.g., the regularization parameters, hypersphere radius, and/or kernel-specific parameters). ... A detailed description of the experimental setup can be found in Appendix C. |