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COLT
2008
Springer
13 years 9 months ago
High-Probability Regret Bounds for Bandit Online Linear Optimization
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, S...
CORR
2010
Springer
165views Education» more  CORR 2010»
13 years 8 months ago
Online Learning: Beyond Regret
We study online learnability of a wide class of problems, extending the results of [26] to general notions of performance measure well beyond external regret. Our framework simult...
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
CVPR
2012
IEEE
11 years 10 months ago
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video
It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision prob...
Jun He, Laura Balzano, Arthur Szlam
ESANN
2007
13 years 9 months ago
Intrinsic plasticity for reservoir learning algorithms
One of the most difficult problems in using dynamic reservoirs like echo state networks for signal processing is the choice of reservoir network parameters like connectivity or spe...
Marion Wardermann, Jochen J. Steil
EOR
2010
93views more  EOR 2010»
13 years 8 months ago
Two-stage flexible-choice problems under uncertainty
A significant input-data uncertainty is often present in practical situations. One approach to coping with this uncertainty is to describe the uncertainty with scenarios. A scenar...
Jurij Mihelic, Amine Mahjoub, Christophe Rapine, B...