LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem

Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin
3/10/2024
cs.LGstat.ML

No official code found. Best implementation: yunangwu/LinearAPT (0 stars, last commit 2025-03-03).

Abstract

In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined threshold under resource constraints. We present LinearAPT, a novel algorithm designed for the fixed budget setting of TLB, providing an efficient solution to optimize sequential decision-making. This algorithm not only offers a theoretical upper bound for estimated loss but also showcases robust performance on both synthetic and real-world datasets. Our contributions highlight the adaptability, simplicity, and computational efficiency of LinearAPT, making it a valuable addition to the toolkit for addressing complex sequential decision-making challenges.

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Code Implementations(1)

yunangwu/LinearAPTCommunity66%

Repository of the PAKDD2025 paper "LinearAPT: An Adaptive Algorithm for the Fixed- Budget Thresholding Linear Bandit Problem"

00Shell, PythonFeb 26, 20251 years ago

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Cite this paper

@article{wu2024linearapt,
  title  = {LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem},
  author = {Yun-Ang Wu and Yun-Da Tsai and Shou-De Lin},
  year   = {2024},
  eprint = {2403.06230},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.06230v1}
}

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