Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based
Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration …

We propose information-directed sampling--a new algorithm for online optimization
problems in which a decision-maker must balance between exploration and exploitation …

We study the problem of using causal models to improve the rate at which good
interventions can be learned online in a stochastic environment. Our formalism combines …

We propose information-directed sampling—a new approach to online optimization
problems in which a decision maker must balance between exploration and exploitation …

We study a general class of online learning problems where the feedback is specified by a
graph. This class includes online prediction with expert advice and the multi-armed bandit …

Stochastic linear bandits with high-dimensional sparse features are a practical model for a
variety of domains, such as personalized medicine and online advertising. We derive a …

Stochastic linear bandits are a natural and simple generalisation of finite-armed bandits with
numerous practical applications. Current approaches focus on generalising existing …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …