Faculty Candidate Seminar

Event Details
  • Date/Time:
    • Monday January 5, 2015
      10:00 am - 11:00 am
  • Location: Advisory Board Room 402 Groseclose
  • Phone:
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  • Fee(s):
    N/A
  • Extras:
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Summaries

Summary Sentence: Faculty Candidate Seminar

Full Summary: No summary paragraph submitted.

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. 

Additional Information

In Campus Calendar
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Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff
Categories
Seminar/Lecture/Colloquium
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Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Dec 29, 2014 - 6:25am
  • Last Updated: Oct 7, 2016 - 10:10pm