2026-09-20T02:35:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1197642026-06-06T00:48:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni.
Gustafson, Laura (Laura N.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-18T19:49:03Z
2018-12-18T19:49:03Z
2018
2018
http://hdl.handle.net/1721.1/119764
1078783823
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-100).
The goal of this thesis is to build an extensible and open source library that handles the problems of tuning the hyperparameters of a machine learning pipeline, selecting between multiple pipelines, and recommending a pipeline. We devise a library that users can integrate into their existing datascience workflows and experts can contribute to by writing methods to solve these search problems. Extending upon the existing library, our goals are twofold: one that the library naturally fits within a user's existing workflow, so that integration does not require a lot of overhead, and two that the three search problems are broken down into small and modular pieces to allow contributors to have maximal flexibility. We establish the abstractions for each of the solutions to these search problems, showcasing how both a user would use the library and a contributor could override the API. We discuss the creation of a recommender system, that proposes machine learning pipelines for a new dataset, trained on an existing matrix of known scores of pipelines on datasets. We show how using such a system can lead to performance gains. We discuss how we can evaluate the quality of different solutions to these types of search problems, and how we can measurably compare them to each other.
by Laura Gustafson.
M.Eng.
100 pages
eng
Massachusetts Institute of Technology
MIT 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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Bayesian tuning and bandits : an extensible, open source library for AutoML
Extensible, open source library for AutoML
Thesis
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