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

arXiv: https://arxiv.org/abs/2403.06230 · HTML: https://mlsanity.com/paper/2403.06230/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 10 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Best-Arm Identification in Linear Bandits](https://mlsanity.com/paper/1409.6110/) (2014)
- [Gamification of Pure Exploration for Linear Bandits](https://mlsanity.com/paper/2007.00953/) (2020)
- [Thresholding Bandit for Dose-ranging: The Impact of Monotonicity](https://mlsanity.com/paper/1711.04454/) (2017)
- [The Influence of Shape Constraints on the Thresholding Bandit Problem](https://mlsanity.com/paper/2006.10006/) (2020)
- [Top-m identification for linear bandits](https://mlsanity.com/paper/2103.10070/) (2021)
- [Differential Good Arm Identification](https://mlsanity.com/paper/2303.07154/) (2023)
- [Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network](https://mlsanity.com/paper/2202.08867/) (2022)
- [Thresholding Bandit with Optimal Aggregate Regret](https://mlsanity.com/paper/1905.11046/) (2019)
- [Thresholding Graph Bandits with GrAPL](https://mlsanity.com/paper/1905.09190/) (2019)
- [Problem Dependent View on Structured Thresholding Bandit Problems](https://mlsanity.com/paper/2106.10166/) (2021)
- [lil'HDoC: An Algorithm for Good Arm Identification under Small Threshold Gap](https://mlsanity.com/paper/2401.15879/) (2024)

## Built on by (most cited first, 2 of 2)

- [Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection](https://mlsanity.com/paper/2505.07309/) (2025)
- [Multimodal Chip Physical Design Engineer Assistant](https://mlsanity.com/paper/2510.15872/) (2025)

## Abstract (arXiv)

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.