Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …

Bandit algorithms are increasingly used in real-world sequential decision-making problems.
Associated with this is an increased desire to be able to use the resulting datasets to answer …

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 …

Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-
commerce, healthcare, and policymaking because they can both improve outcomes for …

Off-policy learning, referring to the procedure of policy optimization with access only to
logged feedback data, has shown importance in various important real-world applications …

In many applications, eg in healthcare and e-commerce, the goal of a contextual bandit may
be to learn an optimal treatment assignment policy at the end of the experiment. That is, to …

Sample-efficient offline reinforcement learning (RL) with linear function approximation has
been studied extensively recently. Much of the prior work has yielded instance-independent …

In a wide variety of applications, including healthcare, bidding in first price auctions, digital
recommendations, and online education, it can be beneficial to learn a policy that assigns …

Experimentation has been critical and increasingly popular across various domains, such as
clinical trials and online platforms, due to its widely recognized benefits. One of the primary …

During online decision making in Multi-Armed Bandits (MAB), one needs to conduct
inference on the true mean reward of each arm based on data collected so far at each step …