# Thresholding Bandit with Optimal Aggregate Regret

arXiv: https://arxiv.org/abs/1905.11046 · HTML: https://mlsanity.com/paper/1905.11046/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 6 of 6)

- [Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits](https://mlsanity.com/paper/2110.09133/) (2021)
- [Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions](https://mlsanity.com/paper/2208.05844/) (2022)
- [Adaptive Experiment Design with Synthetic Controls](https://mlsanity.com/paper/2401.17205/) (2024)
- [LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem](https://mlsanity.com/paper/2403.06230/) (2024)
- [Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation](https://mlsanity.com/paper/2506.03062/) (2025)
- [An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem](https://mlsanity.com/paper/2302.02033/) (2023)

## Abstract (arXiv)

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold θ, with a fixed budget of T trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algorithm is instance-wise asymptotically optimal. We also provide comprehensive empirical results to demonstrate the algorithm's superior performance over existing algorithms under a variety of different scenarios.