# Near-Optimal MNL Bandits Under Risk Criteria

arXiv: https://arxiv.org/abs/2009.12511 · HTML: https://mlsanity.com/paper/2009.12511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

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

- [Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits](https://mlsanity.com/paper/2406.06552/) (2024)

## Abstract (arXiv)

We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general goals widely used in industries and bussiness. We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio and entropy risk, and prove that they suffer a near-optimal regret. As a complement, we also conduct experiments with both synthetic and real data to show the empirical performance of our proposed algorithms.