Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

The major application areas of reinforcement learning (RL) have traditionally been game
playing and continuous control. In recent years, however, RL has been increasingly applied …

Invariant approaches have been remarkably successful in tackling the problem of domain
generalization, where the objective is to perform inference on data distributions different …

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 …

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital
interventions in the fields of mobile health and online education. Common challenges in …

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 …

Multi-armed bandit has been well-known for its efficiency in online decision-making in terms
of minimizing the loss of the participants' welfare during experiments (ie, the regret). In …

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 …

The contextual linear bandit is an important online learning problem where given arm
features, a learning agent selects an arm at each round to maximize the cumulative rewards …

We investigate meta-learning procedures in the setting of stochastic linear bandits tasks.
The goal is to select a learning algorithm which works well on average over a class of …