Fixed Confidence Best Arm Identification in the Bayesian Setting
Authors: Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations verify the theoretical results.We conduct two experiments to demonstrate that the expected stopping times of frequentist δ-correct algorithms diverge in a Bayesian setting and that the elimination process in Algorithm 1 is necessary for more efficient sampling. In Tables 1 and 2, each column Avg , Max , and Error represents the average stopping time, maximum stopping time, and the ratio of the misidentification, respectively. |
| Researcher Affiliation | Academia | Kyoungseok Jang Universitá degli Studi di Milano ksajks@gmail.com Junpei Komiyama New York University / RIKEN AIP junpei@komiyama.info Kazutoshi Yamazaki The University of Queensland k.yamazaki@uq.edu.au |
| Pseudocode | Yes | Algorithm 1 Successive Elimination with Early-Stopping. Algorithm 2 No Elimination (No Elim) Algorithm. |
| Open Source Code | Yes | The code used in the experiments for this paper can be found at the following link: https://github.com/jajajang/FC_BAI_Bayes. |
| Open Datasets | No | The paper describes generating data based on a specified Gaussian prior distribution for simulations (e.g., "k = 2 arms with standard Gaussian prior distribution, which means mi = 0, ξi = 1 for all i [k]" and random numbers for prior means/variances in additional experiments). It does not use a pre-existing publicly available dataset with a concrete link, DOI, or formal citation. |
| Dataset Splits | No | The paper describes a simulation-based experimental setup (e.g., "ran N = 1000 Bayesian FC-BAI simulations") rather than traditional train/validation/test splits from a fixed dataset. |
| Hardware Specification | Yes | Hardware We used Python 3.7 as our programming language and Macbook Pro M2 16 inch as our hardware. |
| Software Dependencies | Yes | Hardware We used Python 3.7 as our programming language and Macbook Pro M2 16 inch as our hardware. |
| Experiment Setup | Yes | We design an experiment setup that has k = 2 arms with standard Gaussian prior distribution, which means mi = 0, ξi = 1 for all i [k]. We set δ = 0.1 and ran N = 1000 Bayesian FC-BAI simulations to estimate the expected stopping time and success rate. |