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dc.contributor.advisorTommi Jaakkola.en_US
dc.contributor.authorDu, George Jen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-12-20T17:24:52Z
dc.date.available2017-12-20T17:24:52Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/112840
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 28-29).en_US
dc.description.abstractThe goal of this research was twofold. The first goal was to use observational data to propose interventions under various constraints, without explicitly inferring a causal graph. These interventions may be optimized for a single individual within a population, or for an entire population. Under certain assumptions, we found that is possible to provide theoretical guarantees for the intervention results when we model the data with a Gaussian process. The second goal was to map various data, including sentences and medical images, to a simple, understandable latent space, in which an intervention optimization routine may be used to nd beneficial interventions. To this end, variational autoencoders were used. We found that while the Gaussian process technique was able to successfully identify interventions in both simulations and practical applications, the variational autoencoder approach did not retain enough information about the input to be competitive with current approaches for classication, such as deep CNNs.en_US
dc.description.statementofresponsibilityby George J. Du.en_US
dc.format.extent29 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.titleInterpreting and optimizing dataen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1015201941en_US


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