Active Search with Complex Actions and Rewards

Abstract

Active search studies algorithms that can find all positive examples in an unknown environment by collecting and learning from labels that are costly to obtain. They start with a pool of unlabeled data, act to design queries, and get rewarded by the number of positive examples found in a long-term horizon. Active search is connected to active learning, multi-armed bandits, and Bayesian optimization.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2017
Accession Number
AD1168010

Entities

People

  • Yifei Ma

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Compressed Sensing
  • Computational Science
  • Computer Languages
  • Data Mining
  • Dimensionality Reduction
  • Information Processing
  • Information Retrieval
  • Information Science
  • Information Systems
  • Information Theory
  • Kernel Functions
  • Machine Learning
  • Network Science
  • Neural Networks
  • Ontologies
  • Supervised Machine Learning
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks