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Accelerating human-in-the-loop machine learning
Xin, Doris
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https://hdl.handle.net/2142/105721
Description
- Title
- Accelerating human-in-the-loop machine learning
- Author(s)
- Xin, Doris
- Issue Date
- 2019-07-18
- Director of Research (if dissertation) or Advisor (if thesis)
- Parameswaran, Aditya G
- 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
- 2019-11-26T20:35:17Z
- Keyword(s)
- data management
- machine learning
- human-in-the-loop analytics
- Abstract
- Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Unfortunately, existing machine learning systems focus narrowly on model training—a small fraction of the overall development time—and neglect to address iterative development. We propose Helix, a machine learning system that optimizes the execution across iterations—intelligently caching and reusing, or recomputing intermediates as appropriate. Helix captures a wide variety of application needs within its Scala DSL, with succinct syntax defining unified processes for data preprocessing, model specification, and learning. We demonstrate that the reuse problem can be cast as a Max-Flow problem, while the caching problem is NP-Hard. We develop effective lightweight heuristics for the latter. Empirical evaluation shows that Helix is not only able to handle a wide variety of use cases in one unified workflow but also much faster, providing run time reductions of up to 19× over state-of-the-art systems, such as DeepDive or KeystoneML, on four real-world applications in natural language processing, computer vision, social and natural sciences.
- Graduation Semester
- 2019-08
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/105721
- Copyright and License Information
- Copyright 2019 Doris Xin
Owning Collections
Dissertations and Theses - Computer Science
Dissertations and Theses from the Siebel School of Computer ScienceGraduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
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