Multi-Armed Bandits with Betting

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 ...

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...

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

The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits

We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with side information). Epoch-Greedy has the following properties: 1. No knowledge of a time horizon T is necessary. 2. The regret incurred by Epoch-Greedy is controlled by a sample complexity bound for a hypothesis class. 3. The regret scales asO(T S) or better (sometimes, much better). Here S is th...