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

• URL: http://arxiv.org/abs/2403.06230v1
• Date: Sun, 10 Mar 2024 15:01:50 GMT
• Title: LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem
• Authors: Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin
• Abstract summary: We present LinearAPT, a novel algorithm designed for the fixed budget setting of the Thresholding Linear Bandit (TLB) problem. 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.
• Score: 4.666048091337632
• License: http://creativecommons.org/licenses/by/4.0/
• 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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