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..
Bayesian online change point detection with Hilbert space approximate Student-t process
Authors: Jeremy Sellier, Petros Dellaportas
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Improvements in prediction and training time are demonstrated with real-world data sets. |
| Researcher Affiliation | Academia | 1Department of Statistical Science, University College London, UK 2Department of Statistics, Univ. of Econ. and Business, Athens, Greece 3The Alan Turing Institute, UK. |
| Pseudocode | Yes | Algorithm 1 BOCPD run length estimation; Algorithm 2 HSSPAR-CP UPM implementation |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The Nile data set records the lowest annual water levels of the Nile river during the period 622-1284. The data has been used for change point detection in Garnett et al. (2009) and Saatc i et al. (2010). The Well Log data set contains 4050 measurements of radioactivity taken during the drilling of a well. These data have been studied in the context of change point detection by Ruanaidh & Fitzgerald (2012) and by Fearnhead & Clifford (2003). |
| Dataset Splits | No | The paper specifies training and test sets for the datasets (e.g., '200 training points, 463 Test points' for Nile Data), but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scipy method linalg.blas.dger for Python' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We use a hazard function with a trainable constant hazard rate h initialized at 100... Our implementations of HSSPAR and HSGPAR use the Hilbert space reduced-rank kernel derived from Gaussian kernels with the number of basis functions m ranging from 5 to 15. For auto-regressive UPM (GPAR and HSSPAR variants), we use lag parameter p = 1, 2, 3. |