Learning to Choose Under a Fixed Budget: Bandits, Adaptivity, and Large Deviations
- 2026-07-27 (Mon.), 10:30 AM
- Auditorium, B1F, Institute of Statistical Science；The tea reception will be held at 10:10.
- Online live streaming through Microsoft Teams will be available.
- Prof. Po-An Wang
- Institute of Statistics and Data Science, National Tsing Hua University, Taiwan
Abstract
In many decision problems, we must choose the best option after only a limited number of trials. Examples include A/B testing, online advertising, recommendation systems, and career exploration. This talk studies such questions through the lens of fixed-budget best-arm identification in stochastic bandits. I will first revisit the basic two-option setting, where the value of adaptivity is more subtle than it may appear. I will then move to the multi-armed setting, where adaptive elimination strategies become natural. Finally, I will explain how large-deviation ideas provide tools for analyzing adaptive sampling algorithms and for understanding both the promise and the limitations of adaptivity under a fixed budget.
Please click here for participating the talk online.
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