Abstract Details
Activity Number:
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685
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #310236 |
Title:
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Nonparametric Estimation in a Bandit Problem with Covariates
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Author(s):
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Wei Qian*+ and Yuhong Yang
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Companies:
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University of Minnesota and University of Minnesota
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Keywords:
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regret bound ;
nonparametric bandit ;
MABC ;
exploration-exploitation tradeoff ;
contextual bandit problem
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Abstract:
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Bandit problem is an optimization game that requires the balance between exploration and exploitation to maximize the total reward. Motivated by important applications in web-based services and clinical research, we consider a problem setting where the mean reward of each arm can be dependent on some covariates. Based on a sequential randomized allocation strategy, we perform the finite-time regret analysis and provide the cumulative regret upper bound for nonparametric reward estimation methods. Simulations and a real data evaluation are conducted to illustrate the performance of the proposed allocation strategy.
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Authors who are presenting talks have a * after their name.
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