A Semi-Parametric Global Bandit Framework for Flexible and Scalable Sequential Decision-Making
Автор: Penn State CTSI
Загружено: 2026-03-02
Просмотров: 11
Описание:
Sequential decision-making is at the heart of personalized medicine and adaptive clinical trials, where treatments must be adjusted as data accumulate. While multi-armed bandit methods are well-suited to these problems, real-world data introduce complications like delayed feedback, patient characteristics, and related treatment options.
In this talk, Dr. Song will present a new framework for batched bandit problems that accounts for these challenges. The approach uses a flexible yet interpretable model to learn how treatments are related and introduces a new algorithm that efficiently narrows down the best options over time.
Through simulations and real-world examples, Dr. Song will show how this method improves decision-making accuracy and outperforms existing approaches in complex clinical settings.
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
