University of Illinois Urbana-Champaign

Towards understanding and simplifying human-in-the-loop machine learning

Ma, Litian

Content Files
MA-THESIS-2018.pdf

Permalink

https://hdl.handle.net/2142/101231

Description

Title
Towards understanding and simplifying human-in-the-loop machine learning
Author(s)
Ma, Litian
Issue Date
2018-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Parameswaran, Aditya
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-09-04T20:41:58Z
Keyword(s)
  • human in the loop computing
  • machine learning
Abstract
"Machine learning application developers and data scientists spend inordinate amount of time iterating on machine learning (ML) workflows, by modifying the data pre-processing, model training, and post-processing steps, via trial-and-error to achieve the desired model performance. As a result, developers are ""in-the-loop"" of the development cycle. Under this ""human-in-the-loop"" setting, the ultimate goal of a ML system becomes shortening the time to obtain deployable models from scratch. However, some of the existing ML systems ignore this iterative aspect, and only optimize the one-shot execution of the workflow, while some of them don't provide enough support for system users to make iterative changes. Here, we first conduct a mini-survey of the applied machine learning literature to quantitatively study the user behavior in iterative ML application development. Then, we propose Helix, a declarative machine learning system implemented in Scala. Helix mainly focuses on the optimization of the execution across iterations by reusing or recomputing intermediate results as appropriate. Finally, we describe our collaboration system on top of Helix, that includes a workflow management module and a visualization tool, to make the machine learning system easier to use. In our evaluations, Helix achieved a 60% magnitude reduction in cumulative running time compared to state-of-the-art machine learning tools."
Graduation Semester
2018-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/101231
Copyright and License Information
Copyright 2018 Litian Ma

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
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