  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.
