# Thompson Sampling for Gaussian Entropic Risk Bandits

arXiv: https://arxiv.org/abs/2105.06960 · HTML: https://mlsanity.com/paper/2105.06960/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Bounds for Risk-sensitive Learning](https://mlsanity.com/paper/2006.08138/) (2020)
- [Thompson Sampling Algorithms for Mean-Variance Bandits](https://mlsanity.com/paper/2002.00232/) (2020)
- [Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits](https://mlsanity.com/paper/1401.1123/) (2014)
- [Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure](https://mlsanity.com/paper/1604.05257/) (2016)
- [Risk-Constrained Thompson Sampling for CVaR Bandits](https://mlsanity.com/paper/2011.08046/) (2020)
- [Constrained regret minimization for multi-criterion multi-armed bandits](https://mlsanity.com/paper/2006.09649/) (2020)

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

- [Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs](https://mlsanity.com/paper/2206.12463/) (2022)
- [Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards](https://mlsanity.com/paper/2606.09191/) (2026)

## Abstract (arXiv)

The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risknotably complicates the basic reward-maximising objectives, in part because there is no universally agreed definition of it. In this paper, we consider an entropic risk (ER) measure and explore the performance of a Thompson sampling-based algorithm ERTS under this risk measure by providing regret bounds for ERTS and corresponding instance dependent lower bounds.