Faculty Candidate Seminar

Event Details
  • Date/Time:
    • Monday January 5, 2015
      10:00 am - 11:00 am
  • Location: Advisory Board Room 402 Groseclose
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Summary Sentence: Faculty Candidate Seminar

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TITLE:  Learning to optimize via efficient experimentation

SPEAKER:  Daniel Russo


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. 

Additional Information

In Campus Calendar

School of Industrial and Systems Engineering (ISYE)

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  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Dec 29, 2014 - 6:25am
  • Last Updated: Oct 7, 2016 - 10:10pm