Locally private online change point detection
Authors: Tom Berrett, Yi Yu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. tom.berrett@warwick.ac.uk Yi Yu Department of Statistics University of Warwick Coventry, CV4 7AL, U.K. yi.yu.2@warwick.ac.uk |
| 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 |