# Discrete Choice Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2310.00562 · HTML: https://mlsanity.com/paper/2310.00562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [An efficient algorithm for learning with semi-bandit feedback](https://mlsanity.com/paper/1305.2732/) (2013)
- [Discrete choice prox-functions on the simplex](https://mlsanity.com/paper/1909.05591/) (2019)
- [Dynamic pricing under nested logit demand](https://mlsanity.com/paper/2101.04486/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits](https://mlsanity.com/paper/2409.20440/) (2024)

## Abstract (arXiv)

This paper establishes a connection between a category of discrete choice models and the realms of online learning and multiarmed bandit algorithms. Our contributions can be summarized in two key aspects. Firstly, we furnish sublinear regret bounds for a comprehensive family of algorithms, encompassing the Exp3 algorithm as a particular case. Secondly, we introduce a novel family of adversarial multiarmed bandit algorithms, drawing inspiration from the generalized nested logit models initially introduced by citetwen:2001. These algorithms offer users the flexibility to fine-tune the model extensively, as they can be implemented efficiently due to their closed-form sampling distribution probabilities. To demonstrate the practical implementation of our algorithms, we present numerical experiments, focusing on the stochastic bandit case.