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..
Adaptive Gaussian Process Change Point Detection
Authors: Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 32,792 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,283 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 | In extensive experiments, we show its versatility and applicability to different classes of change points, demonstrating that it is significantly more accurate than current state-of-the-art alternatives. This section reports the empirical results obtained using ADAGA in the context of CP detection in time series. |
| Researcher Affiliation | Academia | 1Institut de Rob otica i Inform atica Industrial, CSIC-UPC, Barcelona, Spain 2Department of Computer Science, ETH Zurich, Zurich, Switzerland 3Department of Intelligent Systems, KTH, Stockholm, Sweden. |
| Pseudocode | Yes | Algorithm 1 ADAGA |
| Open Source Code | Yes | Code available at https://github.com/lasgroup/adaga. The implementation uses scipy (Virtanen et al., 2020), tensorflow (Abadi et al., 2016) and gpflow (De G. Matthews et al., 2017). |
| Open Datasets | Yes | Secondly, we use some of the real-world benchmarks described by van den Burg & Williams (2020). In our work, we choose the following sets, whose detailed description is reported in the supplementary material: Run Log, Business Inventories, Ozone, Iran s GDP, Argentina s GDP, Japan s GDP. |
| Dataset Splits | No | The paper does not explicitly specify the training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits) for its own experimental setup. |
| Hardware Specification | Yes | All the experiments were performed on a custom laptop (Macbook Pro 2019). |
| Software Dependencies | No | The implementation uses scipy (Virtanen et al., 2020), tensorflow (Abadi et al., 2016) and gpflow (De G. Matthews et al., 2017). The paper provides software names but not explicit version numbers for reproducibility, only citations to their original papers. |
| Experiment Setup | Yes | The minimum window size |S| for ADAGA is set to 15 points. We run ADAGA with a batch size of 1, to achieve maximum resolution in the location of the CPs. To avoid overly conservative estimates, we set δ = 0.6. |