Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons

@inproceedings{Agarwal2017LearningWL,
  title={Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons},
  author={Arpit Agarwal and Shivani Agarwal and Sepehr Assadi and Sanjeev Khanna},
  booktitle={COLT},
  year={2017}
}
In many learning settings, active/adaptive querying is possible, but the number of rounds of adaptivity is limited. We study the relationship between query complexity and adaptivity in identifying the k most biased coins among a set of n coins with unknown biases. This problem is a common abstraction of many well-studied problems, including the problem of identifying the k best arms in a stochastic multi-armed bandit, and the problem of top-k ranking from pairwise comparisons. An r-round… CONTINUE READING
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