The multi-armed bandit (MAB) framework holds great promise for optimizing sequential decisions online as new data arise. For example, it could be used to design adaptive experiments that can result in better participant outcomes and improved …
The multi-armed bandit (MAB) framework holds great promise for optimizing sequential decisions online as new data arise. For example, it could be used to design adaptive experiments that can result in better participant outcomes and improved …
The multi-armed bandit (MAB) framework holds great promise for optimizing sequential decisions online as new data arise. For example, it could be used to design adaptive experiments that can result in better participant outcomes and improved …
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further …
Artificial intelligence tools powered by machine learning have shown considerable improvements in a variety of experimental domains, from education to healthcare. In particular, the reinforcement learning (RL) and the multi-armed bandit (MAB) …
Bandit algorithms such as Thompson sampling (TS) have been put forth for decades as useful tools for conducting adaptively-randomised experiments. By skewing the allocation toward superior arms, they can substantially improve particular outcomes of …
Response-adaptive designs, either based on simple rules, urn models, or bandit problems, are of increasing interest among both theoretical and practical communities. In particular, regret-optimising bandit algorithms like Thompson sampling hold the …
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference. Recent attempts to address these challenges typically impose restrictions on the exploitative nature of …
Multi-armed bandit algorithms such as Thompson sampling (TS) have been put forth for decades as useful tools for optimizing sequential decision-making in online experiments. By skewing the allocation ratio towards superior arms, they can minimize …
Adaptive digital field experiments are continually increasing in their breadth of use in fields like mobile health and digital education. Using adaptive experimentation in education can help not only to explore and eventually compare various arms but …