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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: Learning to optimize via efficient experimentation
SPEAKER: Daniel Russo
ABSTRACT:
The information revolution is spawning systems that require very frequent decisions and provide high volumes of data concerning past outcomes. Fueling the design of algorithms used in such systems is a vibrant research area at the intersection of sequential decision-making and machine learning that addresses how to balance between exploration and exploitation and learn over time to make increasingly effective decisions. In this talk, I will formulate a broad family of such problems that greatly extends the classical multi-armed bandit problem by allowing samples of one action to inform the decision-maker's assessment of other actions. I'll describe the rising importance of this problem class, and then discuss two recent methodological advances. One advance is Thompson sampling, a simple and tractable approach that is provably efficient for many relevant problem classes. The other is information-directed sampling, a new algorithm we propose that is inspired by an information-theoretic perspective and can offer greatly superior statistical efficiently. We provide new insight into both algorithms and establish general theoretical guarantees.