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 …

Machine learning research depends on objectively interpretable, comparable, and
reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites …

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 …

The past two decades have witnessed a surge of new research in the analysis of
randomized experiments. The emergence of this literature may seem surprising given the …

A/B testing is critical for modern technological companies to evaluate the effectiveness of
newly developed products against standard baselines. This paper studies optimal designs …

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 …

Contextual bandit has been widely used for sequential decision-making based on the
current contextual information and historical feedback data. In modern applications, such …

There is a growing interest in using reinforcement learning (RL) to personalize sequences of
treatments in digital health to support users in adopting healthier behaviors. Such sequential …

Experimentation is prevalent in online marketplaces and social networks to assess the
effectiveness of new market intervention. To mitigate the interference among users in an …