# Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit

arXiv: https://arxiv.org/abs/2012.01499 · HTML: https://mlsanity.com/paper/2012.01499/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

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

- [Enhancing Preference-based Linear Bandits via Human Response Time](https://mlsanity.com/paper/2409.05798/) (2024)
- [Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification](https://mlsanity.com/paper/2605.25592/) (2026)

## Abstract (arXiv)

Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade. However, it is a bit surprising that pure exploration, a basic problem in bandit theory, has not been well studied in MNL-bandit so far. In this paper we give efficient algorithms for pure exploration in MNL-bandit. Our algorithms achieve instance-sensitive pull complexities. We also complement the upper bounds by an almost matching lower bound.