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 …
Using bandit algorithms to design response-adaptive trials can optimize participant outcomes, but poses major challenges for statistical inference. Recent attempts to address these challenges typically impose restrictions on the exploitative nature …
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 …
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) …
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 …
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and …