# Diversity-Preserving K-Armed Bandits, Revisited

arXiv: https://arxiv.org/abs/2010.01874 · HTML: https://mlsanity.com/paper/2010.01874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

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

- [The price of unfairness in linear bandits with biased feedback](https://mlsanity.com/paper/2203.09784/) (2022)
- [Fair Allocation in Dynamic Mechanism Design](https://mlsanity.com/paper/2406.00147/) (2024)
- [Bandits in Flux: Adversarial Constraints in Dynamic Environments](https://mlsanity.com/paper/2601.19867/) (2026)

## Abstract (arXiv)

We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction to the setting of linear bandits. We design a UCB algorithm using the specific structure of the setting and show that it enjoys a bounded distribution-dependent regret in the natural cases when the optimal mixed actions put some probability mass on all actions (i.e., when diversity is desirable). The regret lower bounds provided show that otherwise, at least when the model is mean-unbounded, a ln T regret is suffered. We also discuss an example beyond the special case of polytopes.