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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A Kernel Multiple Change-point Algorithm via Model Selection
Authors: Sylvain Arlot, Alain Celisse, Zaid Harchaoui
JMLR 2019 | Venue PDF | LLM Run Details | Input Tokens: 39,899 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 65,522 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | N/A | |
| Researcher Affiliation | N/A | |
| Pseudocode | N/A | |
| Open Source Code | N/A | |
| Open Datasets | N/A | |
| Dataset Splits | N/A | |
| Hardware Specification | N/A | |
| Software Dependencies | N/A | |
| Experiment Setup | N/A |