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AAAI
2006
15 years 8 months ago
A Value Theory of Meta-Learning Algorithms
We use game theory to analyze meta-learning algorithms. The objective of meta-learning is to determine which algorithm to apply on a given task. This is an instance of a more gene...
Abraham Bagherjeiran
ICDE
2009
IEEE
139views Database» more  ICDE 2009»
16 years 8 months ago
Minimizing Communication Cost in Distributed Multi-query Processing
Increasing prevalence of large-scale distributed monitoring and computing environments such as sensor networks, scientific federations, Grids etc., has led to a renewed interest in...
Jian Li, Amol Deshpande, Samir Khuller
SIAMJO
2010
97views more  SIAMJO 2010»
15 years 1 months ago
A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
Abstract. We consider a Newton-CG augmented Lagrangian method for solving semidefinite programming (SDP) problems from the perspective of approximate semismooth Newton methods. In ...
Xin-Yuan Zhao, Defeng Sun, Kim-Chuan Toh
ICASSP
2011
IEEE
14 years 10 months ago
Bounded gradient projection methods for sparse signal recovery
The 2- 1 sparse signal minimization problem can be solved efficiently by gradient projection. In many applications, the signal to be estimated is known to lie in some range of va...
James Hernandez, Zachary T. Harmany, Daniel Thomps...
PODS
2012
ACM
276views Database» more  PODS 2012»
13 years 9 months ago
Randomized algorithms for tracking distributed count, frequencies, and ranks
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k...
Zengfeng Huang, Ke Yi, Qin Zhang