# Learning the distribution with largest mean: two bandit frameworks

arXiv: https://arxiv.org/abs/1702.00001 · HTML: https://mlsanity.com/paper/1702.00001/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Bandits with many optimal arms](https://mlsanity.com/paper/2103.12452/) (2021)
- [Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits](https://mlsanity.com/paper/2110.08627/) (2021)
- [Adaptive Experimentation for Censored Survival Outcomes](https://mlsanity.com/paper/2605.18459/) (2026)

## Abstract (arXiv)

Over the past few years, the multi-armed bandit model has become increasingly popular in the machine learning community, partly because of applications including online content optimization. This paper reviews two different sequential learning tasks that have been considered in the bandit literature ; they can be formulated as (sequentially) learning which distribution has the highest mean among a set of distributions, with some constraints on the learning process. For both of them (regret minimization and best arm identification) we present recent, asymptotically optimal algorithms. We compare the behaviors of the sampling rule of each algorithm as well as the complexity terms associated to each problem.