Corpus ID: 14716432

A Survey on Contextual Multi-armed Bandits

@article{Zhou2015ASO,
  title={A Survey on Contextual Multi-armed Bandits},
  author={Li Zhou},
  journal={ArXiv},
  year={2015},
  volume={abs/1508.03326}
}
  • Li Zhou
  • Published 2015
  • Computer Science, Mathematics
  • ArXiv
In this survey we cover a few stochastic and adversarial contextual bandit algorithms. We analyze each algorithm's assumption and regret bound. 
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References

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Contextual Bandit Algorithms with Supervised Learning Guarantees
Contextual Bandits with Linear Payoff Functions
Thompson Sampling for Contextual Bandits with Linear Payoffs
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
Efficient Optimal Learning for Contextual Bandits
Finite-Time Analysis of Kernelised Contextual Bandits
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Improved Algorithms for Linear Stochastic Bandits
An Empirical Evaluation of Thompson Sampling
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