LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem
<|reference_start|>LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem: 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.<|reference_end|>
@article{wu2024linearapt:,
    title={LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding
  Linear Bandit Problem},
    author={Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin},
    journal={arXiv preprint arXiv:2403.06230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06230},
    primaryClass={cs.LG stat.ML}
}