We consider a generalization of stochastic bandits where the set of arms, X, is allowed to be
a generic measurable space and the mean-payoff function is “locally Lipschitz” with respect …

We consider the framework of stochastic multi-armed bandit problems and study the
possibilities and limitations of strategies that perform an online exploration of the arms. The …

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest
growing area in online advertising. Instead of bulking buying and inventory-centric buying …

We consider the framework of stochastic multi-armed bandit problems and study the
possibilities and limitations of forecasters that perform an on-line exploration of the arms …

In this paper, we consider a very general model for exploration-exploitation tradeoff which
allows arbitrary concave rewards and convex constraints on the decisions across time, in …

We consider the linear contextual bandit problem with resource consumption, in addition to
reward generation. In each round, the outcome of pulling an arm is a reward as well as a …

We consider a contextual version of multi-armed bandit problem with global knapsack
constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource …

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian
optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified …

In this work we study the safe sequential decision making problem under the setting of
adversarial contextual bandits with sequential risk constraints. At each round, nature …

We consider a very general model for managing the exploration–exploitation trade-off,
which allows global convex constraints and concave objective on the aggregate decisions …