Instrument-Armed Bandits

Modal Bandits

Analyses of multi-armed bandits primarily presume that the value of an arm is its expected reward. We introduce a theory for multi-armed bandits where the values are the modes of the reward distributions.

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 ...

Habilitation À Diriger Des Recherches De L'université Paris-est Agrégation Pac-bayésienne Et Bandits À Plusieurs Bras Pac-bayesian Aggregation and Multi-armed Bandits

Semi-Bandits with Knapsacks

We unify two prominent lines of work on multi-armed bandits: bandits with knapsacks and combinatorial semi-bandits. The former concerns limited “resources” consumed by the algorithm, e.g., limited supply in dynamic pricing. The latter allows a huge number of actions but assumes combinatorial structure and additional feedback to make the problem tractable. We define a common generalization, supp...

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...