Bandits with Knapsacks

@inproceedings{Badanidiyuru2013BanditsWK,
  title={Bandits with Knapsacks},
  author={Ashwinkumar Badanidiyuru and Robert D. Kleinberg and Aleksandrs Slivkins},
  booktitle={FOCS},
  year={2013}
}
  • Ashwinkumar Badanidiyuru, Robert D. Kleinberg, Aleksandrs Slivkins
  • Published in FOCS 2013
  • Computer Science
  • Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trials, to communication networks, to Web search and advertising. [...] Key Method We present two algorithms whose reward is close to the information-theoretic optimum: one is based on a novel "balanced exploration" paradigm, while the other is a primal-dual algorithm that uses multiplicative updates. Further, we prove that the regret…Expand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 57 REFERENCES

    Approximation Algorithms for Correlated Knapsacks and Non-martingale Bandits

    Finite-time Analysis of the Multiarmed Bandit Problem

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems

    VIEW 1 EXCERPT

    The Nonstochastic Multiarmed Bandit Problem

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Multi-armed bandits in metric spaces

    VIEW 3 EXCERPTS