We study model selection in linear bandits, where the learner must adapt to the dimension
(denoted by $ d_\star $) of the smallest hypothesis class containing the true linear model …

We present a novel learning framework that utilizes the early exit of Deep Neural Network
(DNN), a device-only solution that reduces the latency of inference by sacrificing a …

In various recommender system applications, from medical diagnosis to dialog systems, due
to observation costs only a small subset of a potentially large number of context variables …

In this paper, we analyze and extend an online learning frame-work known as Context-
Attentive Bandit, motivated by various practical applications, from medical diagnosis to …

We present a novel online decision-making solution, where the optimal path of a given
decision tree is dynamically found based on the contextual bandits analysis. At each round …

In the digital world, more and more autonomous agents make automatic decisions to
optimize a criterion by learning from their past decisions. Their rapid multiplication implies …