Adversarially Robust Change Point Detection
Authors: Mengchu Li, Yi Yu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments are conducted with comparisons to existing robust change point detection methods. |
| Researcher Affiliation | Academia | Mengchu Li Department of Statistics University of Warwick mengchu.li@warwick.ac.uk Yi Yu Department of Statistics University of Warwick yi.yu.2@warwick.ac.uk |
| Pseudocode | Yes | Algorithm 1 Adversarially robust change point detection (ARC) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Air Quality Historical Data Platform, 2018. URL https://aqicn.org/data-platform/ register/. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | As for simulation purpose, we fix h = 170 and λ = max{0.6σ, 8σε}. |