Location of Repository

New formulations for active learning

By Ravi Sastry Ganti Mahapatruni


In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework for active learning in the pool setting, and instantiate this framework by using ideas from learning with experts, stochastic optimization, and multi-armed bandits. For the problem of learning convex combination of a given set of hypothesis, we provide a stochastic mirror descent based active learning algorithm in the stream setting.Ph.D

Topics: Active learning, Sequential analysis, Stochastic optimization, Active learning, Algorithms, Sequential analysis, Mathematical optimization, Machine learning
Publisher: Georgia Institute of Technology
Year: 2014
OAI identifier: oai:smartech.gatech.edu:1853/51801
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://hdl.handle.net/1853/518... (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.