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

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3 Author(s)
Abernethy, J.D. ; Dept. of Comput. Sci., Univ. of California Berkeley, Berkeley, CA, USA ; Hazan, E. ; Rakhlin, A.

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 learning.

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Information Theory, IEEE Transactions on  (Volume:58 ,  Issue: 7 )