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..
Adversarially Robust Change Point Detection
Authors: Mengchu Li, Yi Yu
NeurIPS 2021 | Venue PDF | 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 EMAIL Yi Yu Department of Statistics University of Warwick EMAIL |
| 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}. |