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dc.contributor.advisorKalyan Veeramachaneni.en_US
dc.contributor.authorAnderson, Alec W. (Alec Wayne)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-11T20:38:06Z
dc.date.available2018-12-11T20:38:06Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119509
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.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 105-108).en_US
dc.description.abstractWithin the automated machine learning movement, hyperparameter optimization has emerged as a particular focus. Researchers have introduced various search algorithms and open-source systems in order to automatically explore the hyperparameter space of machine learning methods. While these approaches have been effective, they also display significant shortcomings that limit their applicability to realistic data science pipelines and datasets. In this thesis, we propose an alternative theoretical and implementational approach by incorporating sampling techniques and building an end-to-end automation system, Deep Mining. We explore the application of the Bag of Little Bootstraps to the scoring statistics of pipelines, describe substantial asymptotic complexity improvements from its use, and empirically demonstrate its suitability for machine learning applications. The Deep Mining system combines a standardized approach to pipeline composition, a parallelized system for pipeline computation, and clear abstractions for incorporating realistic datasets and methods to provide hyperparameter optimization at scale.en_US
dc.description.statementofresponsibilityby Alec W. Anderson.en_US
dc.format.extent108 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleDeep Mining : scaling Bayesian auto-tuning of data science pipelinesen_US
dc.title.alternativeScaling Bayesian auto-tuning of data science pipelinesen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1066344216en_US


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