Sample Efficient Cost-Aware Active Learning

by Das, Srijita

Type Thesis
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The grand vision of Artificial Intelligence is to build agents that can continuously learn from experience and reason about how to act in the world. Traditional machine learning models generally assume access to huge amount of data. This allows effective learning from huge historical data. However, certain domains are inherently sparse, for example, healthcare. It is essential to build machine learning models that can learn and reason effectively in such sparse domains.

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Summary

The grand vision of Artificial Intelligence is to build agents that can continuously learn from experience and reason about how to act in the world. Traditional machine learning models generally assume access to huge amount of data. This allows effective learning from huge historical data. However, certain domains are inherently sparse, for example, healthcare. It is essential to build machine learning models that can learn and reason effectively in such sparse domains.

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Edition Statements

  • Publication Date:2020

Thesis Information

  • Degree:Ph.D.
  • University:The University of Texas at Dallas
  • Advisor/Supervisor:Natarajan, Sriraam
  • Pages:154

Publication Statements

  • Language:eng
  • Publication Date:2020

Edition Identifiers

  • proquest:2489323436

Subjects

Active learning artificial intelligence Computer engineering Computer Science eng Machine learning PDF Reinforcement learning

Writers

Das, Srijita Natarajan, Sriraam

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