Sharp bounds on the price of bandit feedback for several models of mistake-bounded online learning

  title={Sharp bounds on the price of bandit feedback for several models of mistake-bounded online learning},
  author={Raymond Feng and Jesse T. Geneson and Andrew Lee and Espen Slettnes},
We determine sharp bounds on the price of bandit feedback for several variants of the mistake-bound model. The first part of the paper presents bounds on the r -input weak reinforcement model and the r -input delayed, ambiguous reinforcement model. In both models, the adversary gives r inputs in each round and only indicates a correct answer if all r guesses are correct. The only difference between the two models is that in the delayed, ambiguous model, the learner must answer each input before… 



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Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

  • N. Littlestone
  • Computer Science
    28th Annual Symposium on Foundations of Computer Science (sfcs 1987)
  • 1987
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