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dc.contributor.advisorStefanie Jegelka and Leslie Pack Kaelbling.en_US
dc.contributor.authorWang, Zi, Ph.D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-07-18T19:11:18Z
dc.date.available2016-07-18T19:11:18Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103668
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 77-80).en_US
dc.description.abstractOptimizing an expensive unknown function is an important problem addressed by Bayesian optimization. Motivated by the challenge of parameter tuning in both machine learning and robotic planning problems, we study the maximization of a black-box function with the assumption that the function is drawn from a Gaussian process (GP) with known priors. We propose an optimization strategy that directly uses a maximum a posteriori (MAP) estimate of the argmax of the function. This strategy offers both practical and theoretical advantages: no tradeoff parameter needs to be selected, and, moreover, we establish close connections to the popular GPUCB and GP-PI strategies. GP-UCB and GP-PI may be viewed as special cases of MAP estimation; while, conversely, MAP criterion can be understood as automatically and adaptively trading off exploration and exploitation in GP-UCB and GP-PI. We illustrate the effects of this adaptive tuning both theoretically and empirically. We establish tighter regret bounds than previous methods, as well as an upper bound on the number of steps necessary to achieve a low regret. In our experiments, we show an extensive empirical evaluation on robotics and vision tasks, demonstrating the robustness of this strategy for a range of performance criteria.en_US
dc.description.statementofresponsibilityby Zi Wang.en_US
dc.format.extent80 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleOptimization as estimation with Gaussian processes in bandit settingsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc953457064en_US


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