# Top-m identification for linear bandits

arXiv: https://arxiv.org/abs/2103.10070 · HTML: https://mlsanity.com/paper/2103.10070/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Best-Arm Identification in Linear Bandits](https://mlsanity.com/paper/1409.6110/) (2014)
- [Sequential Experimental Design for Transductive Linear Bandits](https://mlsanity.com/paper/1906.08399/) (2019)
- [Pure Exploration with Multiple Correct Answers](https://mlsanity.com/paper/1902.03475/) (2019)
- [Gamification of Pure Exploration for Linear Bandits](https://mlsanity.com/paper/2007.00953/) (2020)
- [Fully adaptive algorithm for pure exploration in linear bandits](https://mlsanity.com/paper/1710.05552/) (2017)
- [Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection](https://mlsanity.com/paper/1702.03605/) (2017)

## Built on by (most cited first, 11 of 11)

- [Information-Directed Selection for Top-Two Algorithms](https://mlsanity.com/paper/2205.12086/) (2022)
- [Sample Efficient Demonstration Selection for In-Context Learning](https://mlsanity.com/paper/2506.08607/) (2025)
- [Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification](https://mlsanity.com/paper/2111.01479/) (2021)
- [Meta-Learning for Simple Regret Minimization](https://mlsanity.com/paper/2202.12888/) (2022)
- [On Elimination Strategies for Bandit Fixed-Confidence Identification](https://mlsanity.com/paper/2205.10936/) (2022)
- [LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem](https://mlsanity.com/paper/2403.06230/) (2024)
- [Choosing Answers in varepsilon-Best-Answer Identification for Linear Bandits](https://mlsanity.com/paper/2206.04456/) (2022)
- [Best-Arm Identification with Generative Proxy](https://mlsanity.com/paper/2607.06879/) (2026)
- [Efficient Learning for Selecting Top-m Context-Dependent Designs](https://mlsanity.com/paper/2305.04086/) (2023)
- [Challenger-Based Combinatorial Bandits for Subcarrier Selection in OFDM Systems](https://mlsanity.com/paper/2510.04559/) (2025)
- [Data Efficient Sample Selection for In-Context Learning](https://mlsanity.com/paper/2609.06670/) (2026)

## Abstract (arXiv)

Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m ≥ 1 arms with largest means in a linear bandit model, in the fixed-confidence setting. These algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA) that we introduce for Top-m identification in linear bandits. We propose a unified analysis of these algorithms, which shows how the use of features might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.