Sciweavers

Share
24 search results - page 1 / 5
» On the Generalization Ability of Online Strongly Convex Prog...
Sort
View
NIPS
2008
11 years 2 months ago
On the Generalization Ability of Online Strongly Convex Programming Algorithms
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...
Sham M. Kakade, Ambuj Tewari
ICML
2003
IEEE
12 years 2 months ago
Online Convex Programming and Generalized Infinitesimal Gradient Ascent
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...
Martin Zinkevich
CVPR
2010
IEEE
11 years 10 months ago
Online-Batch Strongly Convex Multi Kernel Learning
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-theart perform...
Francesco Orabona, Jie Luo, Barbara Caputo
NIPS
2008
11 years 2 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
NIPS
2007
11 years 2 months ago
Adaptive Online Gradient Descent
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 k...
Peter L. Bartlett, Elad Hazan, Alexander Rakhlin
books