Li Qin
Li is Principal Data Scientist at CVS Health. Li has PhD in Biostatistics from UPENN and 15+ years’ experience creating advanced DS & ML solutions for challenging, complex real problems in academia and industry.
Sessions
11-09
11:45
45min
Contextual Multi-Arm Bandit and its applications to digital experiments
Li Qin, Reed Peterson
Multi-Arm Bandit (MAB) is a reinforcement learning method that seeks to quickly converge to the best action among a set of candidate actions and is often used as an alternative to AB testing. We have developed a custom implementation of Contextual MAB that makes use of context to allow optimization at a more granular and personalized level. We will provide a high-level overview of this methodology and demonstrate its success through use cases relating to digital experiments.
Radio City (6th floor)