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..
Locally private online change point detection
Authors: Tom Berrett, Yi Yu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present the results of a numerical study of our locally private method s performance. |
| Researcher Affiliation | Academia | Thomas Berrett Department of Statistics University of Warwick Coventry, CV4 7AL, U.K. EMAIL Yi Yu Department of Statistics University of Warwick Coventry, CV4 7AL, U.K. EMAIL |
| Pseudocode | Yes | Algorithm 1 Online change point detection via CUSUM statistics and Algorithm 2 Online change point detection via CUSUM statistics |
| Open Source Code | Yes | Full details of the implementation and simulation study can be found in the code available online. The supplementary material contains all the technical details and code of this paper. |
| Open Datasets | No | The paper uses synthetic data generated for the numerical study ('raw data (X1, Y1), . . . , (Xn, Yn) with n = 10000, = 5000, Xi Unif[0, 1] and Yi Unif[mi(x) 1/2, mi(x) + 1/2]'). This data is generated by the authors for their simulation and not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It describes generating synthetic data and using permutations to select thresholds, but not a fixed data split for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or specific computing infrastructure) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | The choices M = 1 and h = 0.2 are used to privatise the data and calculate the test statistics. We permute this sample B = 1000 times to choose our thresholds, as follows: for a range of values of C we run the modified Algorithm 1 on each permutation of the privatised data with the choice t h2α2 C2 log t γh ; , otherwise and we choose the minimal value of C for which the overall false alarm probability is bounded above by γ = 0.1. With the thresholds chosen we ran the experiment over 1000 repetitions |