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.
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 |