Provable Optimal Algorithms for Generalized Linear Contextual Bandits

@inproceedings{Li2017ProvableOA,
  title={Provable Optimal Algorithms for Generalized Linear Contextual Bandits},
  author={Lihong Li and Yu Lu and Dengyong Zhou},
  booktitle={ICML},
  year={2017}
}
Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance than linear models in many applications where rewards are binary. However, most theoretical analyses on contextual bandits so far are on linear bandits. In this work, we propose an upper confidence bound based algorithm for generalized linear contextual bandits, which achieves an… CONTINUE READING

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References

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SHOWING 1-10 OF 31 REFERENCES

Introduction to the non-asymptotic analysis of random matrices

  • Vershynin, Roman
  • Compressed Sensing: Theory and Applications,
  • 2012

Regret analysis of stochastic and nonstochastic multi-armed bandit problems

  • Bubeck, Sébastien, Cesa-Bianchi, Nicolo
  • Foundations and Trends in Machine Learning,
  • 2012

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