Learning and Optimization with Seasonal Patterns
Ningyuan Chen
University of Toronto
A standard assumption adopted in the multi-armed bandit (MAB) framework is that the mean rewards are constant over time. This assumption can be restrictive in the business world as decision-makers often face an evolving environment where the mean rewards are time-varying. Ningyuan Chen discusses a non-stationary MAB model with K arms whose mean rewards vary over time in a periodic manner.
Transcript
Related Videos
Goldsmiths University of London
Experience Driven Design of Creative Systems
Goldsmiths University of London
Constructionist Learning for Student Coders
Uncategorized
Data-driven Learning in an Incremental Grammar Framework
