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» Parameterized Complexity and Approximation Algorithms
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ICALP
2005
Springer
15 years 9 months ago
How Well Can Primal-Dual and Local-Ratio Algorithms Perform?
We define an algorithmic paradigm, the stack model, that captures many primal-dual and local-ratio algorithms for approximating covering and packing problems. The stack model is ...
Allan Borodin, David Cashman, Avner Magen
NIPS
2008
15 years 5 months ago
Continuously-adaptive discretization for message-passing algorithms
Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm for approximate inference. Most message-passing algorithms approximate continu...
Michael Isard, John MacCormick, Kannan Achan
JMLR
2008
137views more  JMLR 2008»
15 years 3 months ago
Online Learning of Complex Prediction Problems Using Simultaneous Projections
We describe and analyze an algorithmic framework for online classification where each online trial consists of multiple prediction tasks that are tied together. We tackle the prob...
Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer
CP
2006
Springer
15 years 7 months ago
Constraint Satisfaction with Bounded Treewidth Revisited
Abstract. The constraint satisfaction problem can be solved in polynomial time for instances where certain parameters (e.g., the treewidth of primal graphs) are bounded. However, t...
Marko Samer, Stefan Szeider
CDC
2010
IEEE
139views Control Systems» more  CDC 2010»
14 years 11 months ago
An adaptive-covariance-rank algorithm for the unscented Kalman filter
Abstract-- The Unscented Kalman Filter (UKF) is a nonlinear estimator that is particularly well suited for complex nonlinear systems. In the UKF, the error covariance is estimated ...
Lauren E. Padilla, Clarence W. Rowley