Near-Optimal Confidence Sequences for Bounded Random Variables
Authors: Arun K Kuchibhotla, Qinqing Zheng
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct numerical experiments to verify our theoretical claims. Moreover, we apply the Bentkus confidence sequence to the pε, δq mean estimation problem and the best-arm identification problem. |
| Researcher Affiliation | Collaboration | 1 Department of Statistics and Data Science, Carnegie Mellon University. 2Facebook AI Research. |
| Pseudocode | Yes | Algorithm 1: Adaptive Stopping Algorithm; Algorithm 2: Best Arm Identification |
| 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 generates synthetic data for its experiments (e.g., "We generate samples Y1, Y2, . . . , Y20000 i.i.d Bernoullip0.1q" and "The data samples are i.i.d generated as Yi m 1 řm j 1 Uij, where Uij are i.i.d uniformly distributed in r0, 1s"). It does not use or provide access to any publicly available external datasets. |
| Dataset Splits | No | The paper describes sequential data generation and does not specify traditional training, validation, or test splits. It focuses on sequential stopping criteria. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We use δ 0.05 for all the experiments. For A-Bentkus, we fix the spacing parameter η 1.1, the stitching function hpkq pk 1q1.1ζp1.1q, and δ1 2δ{3, δ2 δ{3. |