Interior-Point Methods for Full-Information and Bandit Online Learning

@article{Abernethy2012InteriorPointMF,
  title={Interior-Point Methods for Full-Information and Bandit Online Learning},
  author={Jacob D. Abernethy and Elad Hazan and Alexander Rakhlin},
  journal={IEEE Transactions on Information Theory},
  year={2012},
  volume={58},
  pages={4164-4175}
}
We study the problem of predicting individual sequences with linear loss with full and partial (or bandit) feed- back. Our main contribution is the first efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal Õ(√(T)) regret. In addition, for the full-information setting, we give a novel regret minimization algorithm. These results are made possible by the introduction of interior-point methods for convex optimization to online… CONTINUE READING
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