Thompson sampling

Finite-sample and Asymptotic Error Control of a Novel Test for Response-adaptive Designs (Invited Talk @ ICSDS2024)

It is now commonly known that using optimal response-adaptive designs for data collection offers great potential in terms of optimizing expected outcomes, but poses multiple challenges for inferential goals. In many settings, such as phase-II or …

Online sequential-decision making via bandit algorithms, modeling considerations for better decisions (Invited Talk @ BMS-ANed)

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 …

Online sequential-decision making via bandit algorithms, modeling considerations for better decisions (Seminar @ Department of Statistics, Padua University)

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 …

Online sequential-decision making via bandit algorithms, modeling considerations for better decisions (Keynote Talk @ ALBECS-2024, 19th International Conference on Persuasive Technology 2024)

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 …

Reinforcement learning for sequential decision-making. From healthcare to finance (Talk @ Sapienza University)

Thompson sampling for count data in mobile health trials (Invited Talk @ University of Cambridge)

Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such …

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health

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 …

Multinomial Thompson sampling for rating scales and prior considerations for calibrating uncertainty

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 …

Multinomial Thompson Sampling for Online Sequential Decision Making with Rating Scales (Invited Seminar @ Federico II di Napoli)

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 Experiments for Enhancing Digital Education -- Benefits and Statistical Challenges (Talk @ ICNA-STA2023)

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 …