Reducing sequential change detection to sequential estimation
Authors: Shubhanshu Shekhar, Aaditya Ramdas
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically compare the performance of the our RCS-Detector with the BCS-Detector scheme of Shekhar & Ramdas (2023) on the problem of detecting changes in mean of bounded observations. |
| Researcher Affiliation | Academia | 1Department of Statistics and Data Science, Carnegie Mellon University 2Machine Learning Department, Carnegie Mellon University. |
| Pseudocode | Yes | Definition 2.1 (RCS-Detector). Suppose we are given a stream of observations X1, X2, . . ., and a functional θ associated with the source. For Problem 1.1 (non-partitioned), define the C(0) n = Θ for all n ≥ 1, while for Problem 1.2 (partitioned) set C(0) n = Θ0, for all n ≥ 1. Proceed as follows for n = 1, 2, . . . : |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: 'We consider the problem of detecting changes in the mean of bounded observations, where X1, X2, . . . , are independent observations, supported on X = [0, 1]. For t ≤ T, each Xt is drawn according to a Beta distribution... For t > T, each Xt is drawn from a Beta distribution...' It does not refer to or provide access information for a publicly available, pre-existing dataset. |
| Dataset Splits | No | The paper describes generating sequential data streams for simulations and experiments ('For t ≤ T, each Xt is drawn according to a Beta distribution... For t > T, each Xt is drawn from a Beta distribution...'). It does not define or use standard train, validation, or test splits typical for pre-existing datasets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud instance types) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Hoeffding CS' and 'betting CS' from Waudby-Smith & Ramdas (2023), but it does not specify any software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | For this, we set T = 500, and consider five values of α in the set {10−i : 1 ≤ i ≤ 5}, and estimate the ARL as the average of the stopping times over 50 trials (capped at 50000). |