Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Robust and Efficient Kernel Hyperparameter Paths with Guarantees
Authors: Joachim Giesen, Soeren Laue, Patrick Wieschollek
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,109 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,387 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results for these problems applied to various data sets conο¬rm the theoretical complexity analysis. |
| Researcher Affiliation | Academia | Joachim Giesen EMAIL S oren Laue EMAIL Patrick Wieschollek EMAIL Friedrich-Schiller-Universit at Jena, Germany |
| Pseudocode | No | The algorithm is described in Section 3 in prose, but no structured pseudocode or algorithm block is provided. |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of its own source code for the described methodology. |
| Open Datasets | Yes | all data sets that have been used in our experiments were retrieved from the LIBSVM Website, see (Lin). LIBSVM Tools. Data sets available at www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/. |
| Dataset Splits | No | The paper mentions using a 'test data set' but does not specify train/test/validation splits (e.g., percentages or sample counts) or cross-validation details for the datasets. |
| Hardware Specification | No | The paper mentions using MATLAB as a test environment but does not specify any hardware details like CPU/GPU models, memory, or specific computing resources. |
| Software Dependencies | Yes | LIBSVM Version 3.17, whose implementation is described in (Fan et al., 2005), has been used to compute primal-dual optimal pairs. |
| Experiment Setup | Yes | The regularization parameter c was set to 0.1 in all the experiments. The regularization parameter Ξ» was set to 0.1 in the experiments. |