# Generalized Risk-Aversion in Stochastic Multi-Armed Bandits

arXiv: https://arxiv.org/abs/1405.0833 · HTML: https://mlsanity.com/paper/1405.0833/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2014.

## Built on by (most cited first, 11 of 11)

- [Off-Policy Risk Assessment in Contextual Bandits](https://mlsanity.com/paper/2104.08977/) (2021)
- [The Fragility of Optimized Bandit Algorithms](https://mlsanity.com/paper/2109.13595/) (2021)
- [Almost Optimal Variance-Constrained Best Arm Identification](https://mlsanity.com/paper/2201.10142/) (2022)
- [Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk](https://mlsanity.com/paper/2304.04341/) (2023)
- [Supervised Learning with General Risk Functionals](https://mlsanity.com/paper/2206.13648/) (2022)
- [A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits](https://mlsanity.com/paper/2206.02969/) (2022)
- [The Survival Bandit Problem](https://mlsanity.com/paper/2206.03019/) (2022)
- [Functional Linear Regression of Cumulative Distribution Functions](https://mlsanity.com/paper/2205.14545/) (2022)
- [Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs](https://mlsanity.com/paper/2206.12463/) (2022)
- [A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2310.19821/) (2023)
- [Softmax gradient policy for variance minimization and risk-averse multi armed bandits](https://mlsanity.com/paper/2604.00241/) (2026)

## Abstract (arXiv)

We consider the problem of minimizing the regret in stochastic multi-armed bandit, when the measure of goodness of an arm is not the mean return, but some general function of the mean and the variance.We characterize the conditions under which learning is possible and present examples for which no natural algorithm can achieve sublinear regret.