Bandit Quickest Changepoint Detection
Authors: Aditya Gopalan, Braghadeesh Lakshminarayanan, Venkatesh Saligrama
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then perform a number of experiments on synthetic and real datasets demonstrating the effectiveness of our proposed method. ... We perform numerical experiments of ϵ-GCD on synthetic and real datasets and show that under variations of changepoints, anomalies, and action sets, we realize gains due to adaptive sensing. ... Our goal in this section is to illustrate various aspects of our theory through experiments on synthetic environments, and to explore the performance of the proposed ϵ-GCD algorithm on a setting based on a real-world dataset. |
| Researcher Affiliation | Academia | Aditya Gopalan Electrical Communication Engineering Indian Institute of Science, Bengaluru 560012 aditya@iisc.ac.in Braghadeesh Lakshminarayanan Electrical Communication Engineering Indian Institute of Science, Bengaluru 560012 braghadeesh94@gmail.com Venkatesh Saligrama Electrical and Computer Engineering Boston University, Boston, MA 02215 srv@bu.edu |
| Pseudocode | Yes | Algorithm 1 ϵ-GCD |
| Open Source Code | No | The paper does not provide a direct link to source code or explicitly state that the code for the methodology is being released. |
| Open Datasets | Yes | We experiment using the MIMII audio dataset [Pur+19b]. |
| Dataset Splits | No | The paper describes how the MIMII audio dataset was processed (e.g., concatenating files), but does not specify explicit train/test/validation dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All experiments were performed on a laptop with Intel Core i5 CPU and 8GB of RAM, and take under an hour to execute. |
| Software Dependencies | No | The paper mentions training autoencoders using mel-spectrogram features and fitting Gaussians, but it does not specify any software libraries or packages with version numbers used for these tasks (e.g., TensorFlow, PyTorch, Scikit-learn, etc. with versions). |
| Experiment Setup | Yes | We choose σ2 = 0.5, β such that the false alarms are about 1% for the Oracle, and a forced exploration rate ϵ = 0.2. Our results are averaged over 5000 Monte-Carlo runs. |