Organizational dependency on Information and Communication Technology (ICT) drives the
preparedness challenge to cope with business process disruptions. Business Continuity …

Thompson sampling has been widely used for contextual bandit problems due to the
flexibility of its modeling power. However, a general theory for this class of methods in the …

Efficient exploration in bandits is a fundamental online learning problem. We propose a
variant of Thompson sampling that learns to explore better as it interacts with bandit …

Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …

High-quality recommender systems ought to deliver both innovative and relevant content
through effective and exploratory interactions with users. Yet, supervised learning-based …

Abstract We propose AdaTS, a Thompson sampling algorithm that adapts sequentially to
bandit tasks that it interacts with. The key idea in AdaTS is to adapt to an unknown task prior …

How to explore efficiently is a central problem in multi-armed bandits. In this paper, we
introduce the metadata-based multi-task bandit problem, where the agent needs to solve a …

Mean rewards of actions are often correlated. The form of these correlations may be
complex and unknown a priori, such as the preferences of users for recommended products …

We study the problem of learning a mixture of multiple linear dynamical systems (LDSs) from
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …

Wireless links adapt the data transmission parameters to the dynamic channel state–this is
called link adaptation. Classical link adaptation relies on tuning parameters that are …