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NIPS
2008
11 years 3 months ago
Mind the Duality Gap: Logarithmic regret algorithms for online optimization
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 bound...
Shai Shalev-Shwartz, Sham M. Kakade
JMLR
2012
9 years 4 months ago
Beyond Logarithmic Bounds in Online Learning
We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
Francesco Orabona, Nicolò Cesa-Bianchi, Cla...
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