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 [1].
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Authors: Reda Alami, Odalric Maillard, Raphael Feraud
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and realworld data show that this proposal outperforms the state-of-art change-point detection strategy |
| Researcher Affiliation | Collaboration | 1Total S.A. 2INRIA-SCOOL Team 3Orange Labs. |
| Pseudocode | Yes | Algorithm 1 BOCPD (Fearnhead & Liu, 2007); Algorithm 2 R-BOCPD |
| Open Source Code | Yes | Software and simulation code is available at https://github.com/Ralami1859/Restarted-BOCPD. |
| Open Datasets | Yes | These data have been studied in the context of change-point detection by (Fearnhead & Clifford, 2003) and has become a benchmark data set for uni-variate changepoint detection. |
| Dataset Splits | No | No explicit details on train/validation/test splits, percentages, or cross-validation were provided for the datasets used. |
| Hardware Specification | No | No specific hardware (e.g., CPU, GPU models, or cloud instance types) used for experiments was mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with versions) were provided. |
| Experiment Setup | Yes | In all the experiment, we choose ηr,s,t = 1 nr:t for RBOCPD and h = 3/1200 for BOCPD. The curves are averaged over 300 runs. (Their error bars are also plotted). The parameter δ (false alarm rate of Imp GLR) is set to 0.01. |