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Probe-Efficient Learning

  • Author / Creator
    Zolghadr, Navid
  • This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full loss function for this instance that he is evaluated against. Either way, the learner pays for the errors of its predictions, and the cost of observing the features and loss function. We consider two variations of this problem, depending on whether the learner can observe the label for free. We provide algorithms and upper and lower bounds of the regret for both variants. We show that the paying a positive cost for the label significantly increases the regret of the problem. At the end we also convert the online algorithms to variants for batch settings.

  • Subjects / Keywords
  • Graduation date
    2013-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R36C9X
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Specialization
    • Statistical Machine Learning
  • Supervisor / co-supervisor and their department(s)
    • Szepesvari, Csaba (Computing Science)
    • Greiner ,Russell (Computing Science)
  • Examining committee members and their departments
    • Bulitko, Vadim (Computing Science)
    • Schuurmans, Dale (Computing Science)