Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We introduce a novel algorithmic approach to content recommendation based on adaptive
clustering of exploration-exploitation (“bandit") strategies. We provide a sharp regret …

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 …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation
tasks that implements the underlying feedback sharing mechanism by estimating user …

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 …

We present an approach to Intelligent Tutoring Systems which adaptively personalizes
sequences of learning activities to maximize skills acquired by students, taking into account …

We study the problem of representational transfer in RL, where an agent first pretrains in a
number of\emph {source tasks} to discover a shared representation, which is subsequently …