Adaptive online gradient descent

Bartlett, Peter L., Hazan, Elad, & Rakhlin, Alexander (2007) Adaptive online gradient descent. In Platt, J.C., Koller, D., Singer, Y., & Roweis, S.T. (Eds.) Advances in Neural Information Processing Systems 20, MIT Press, Canada, pp. 65-72.

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We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

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ID Code: 81815
Item Type: Conference Paper
Refereed: Yes
Additional Information: Fulltext freely available see link above
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Keywords: OAVJ
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright © 2007, by the author(s).
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Deposited On: 15 Feb 2015 22:18
Last Modified: 19 Feb 2015 17:30

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