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» Competing in the Dark: An Efficient Algorithm for Bandit Lin...
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76
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COLT
2008
Springer
14 years 11 months ago
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O ( T) regret. The setting is a natural general...
Jacob Abernethy, Elad Hazan, Alexander Rakhlin
80
Voted
STOC
2007
ACM
146views Algorithms» more  STOC 2007»
15 years 9 months ago
Playing games with approximation algorithms
In an online linear optimization problem, on each period t, an online algorithm chooses st S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adve...
Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett
98
Voted
ICML
2006
IEEE
15 years 10 months ago
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
Mauro Maggioni, Sridhar Mahadevan
TNN
2008
128views more  TNN 2008»
14 years 9 months ago
A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach
Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient FastICA (EFICA) offers an ...
Petr Tichavský, Zbynek Koldovský, Ar...
CVPR
2010
IEEE
15 years 26 days ago
Discriminative K-SVD for Dictionary Learning in Face Recognition
In a sparse-representation-based face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while...
Qiang Zhang, Baoxin Li