Robust Risk-Averse Stochastic Multi-armed Bandits

A Survey on Contextual Multi-armed Bandits

4 Stochastic Contextual Bandits 6 4.1 Stochastic Contextual Bandits with Linear Realizability Assumption . . . . 6 4.1.1 LinUCB/SupLinUCB . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.1.2 LinREL/SupLinREL . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.3 CofineUCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.4 Thompson Sampling with Linear Payoffs...

Risk-Aversion in Multi-armed Bandits

Stochastic multi–armed bandits solve the Exploration–Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expected reward is not the most desirable objective. In this paper, we introduce a novel setting based on the principle of risk–aversion where the objective is to compete against the arm with the best risk–return trade–off...

Generalized Risk-Aversion in Stochastic Multi-Armed Bandits

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.

Generic Exploration and K-armed Voting Bandits

We study a stochastic online learning scheme with partial feedback where the utility of decisions is only observable through an estimation of the environment parameters. We propose a generic pure-exploration algorithm, able to cope with various utility functions from multi-armed bandits settings to dueling bandits. The primary application of this setting is to offer a natural generalization of ...

Anytime optimal algorithms in stochastic multi-armed bandits - Supplementary Material