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 …
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 …
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 …