@inproceedings{1553e1a25a7a4ca8a192b8755ea27d37,
title = "Mind the duality gap: Logarithmic regret algorithms for online optimization",
abstract = "We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bounds for Follow-The-Leader and for the gradient descent algorithm proposed in Hazan et al. [2006]. We then show that one can interpolate between these two extreme cases. In particular, we derive a new algorithm that shares the computational simplicity of gradient descent but achieves lower regret in many practical situations. Finally, we further extend our framework for generalized strongly convex functions.",
author = "Kakade, {Sham M.} and Shai Shalev-Shwartz",
year = "2009",
language = "American English",
isbn = "9781605609492",
series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
publisher = "Neural Information Processing Systems",
pages = "1457--1464",
booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",
note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",
}