Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
Authors: Yunlong Hou, Vincent Tan, Zixin Zhong
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare PSεBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. |
| Researcher Affiliation | Academia | Yunlong Hou Department of Mathematics National University of Singapore EMAIL Vincent Y. F. Tan Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore EMAIL Zixin Zhong Data Science and Analytics Thrust Hong Kong University of Science and Technology (Guangzhou) EMAIL |
| Pseudocode | Yes | Algorithm 1 PIECEWISE-STATIONARY ε-BEST ARM IDENTIFICATION (PSεBAI) |
| Open Source Code | Yes | All the code to reproduce our experiments can be found at https://github.com/Y-Hou/BAI-in-PSLB.git. |
| Open Datasets | No | We utilize the instance defined in Example 1 with d = 2, ϕ = π/8, We generate a changepoint sequence C such that cl+1 = cl + Ll with Lmin = 3 10^4, Lmax = 5 10^4, P[Ll = Lmin] = 0.8, P[Ll = Lmax] = 0.2, and fix it throughout the whole set of experiments. |
| Dataset Splits | No | The paper uses a synthetic instance and describes how it is generated, but does not specify explicit training/test/validation dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All experiments are conducted via MATLAB R2021b on a Mac Book Pro with Apple M1 Pro chip and 16 GB memory. |
| Software Dependencies | Yes | All experiments are conducted via MATLAB R2021b |
| Experiment Setup | Yes | We set the confidence parameter δ = 0.05 and vary the slackness parameter ε from 0.04 to 0.6 (i.e., ε = 0.03 1.35k for k [12]). We set γ = 6, the window size w = Lmin/(3γ) and compute b via (3.5) in Assumption 1. |