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..
A Kernel Independence Test for Random Processes
Authors: Kacper Chwialkowski, Arthur Gretton
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tests on artificial data and real-world forex data indicate that the new test procedure discovers dependence which is missed by linear approaches, while the earlier bootstrap procedure returns an elevated number of false positives. |
| Researcher Affiliation | Academia | Kacper Chwialkowski EMAIL University College London, Computer Science Department Arthur Gretton EMAIL University College London, Gatsby Computational Neuroscience Unit |
| Pseudocode | Yes | Algorithm 1 Generate innovations |
| Open Source Code | No | The paper does not provide any statements or links indicating that source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions 'artificial data' and 'real-world forex data' but does not provide specific links, DOIs, repository names, or formal citations for publicly available datasets. |
| Dataset Splits | No | The paper mentions 'sample size 1200' and '720 samples' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | Processes used in this experiment had an autoregressive component of 0.2, and the radius of the innovation process was 1. We set an extinction rate to 50%. The AR component a in the model (6) controls the memory of a processes the larger this component, the longer the memory. We set an extinction rate to 50%. |