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» Adaptive Bound Optimization for Online Convex Optimization
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JMLR
2012
11 years 10 months ago
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Sparse additive models are families of d-variate functions with the additive decomposition f∗ = ∑j∈S f∗ j , where S is an unknown subset of cardinality s d. In this paper,...
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
ICML
2009
IEEE
14 years 8 months ago
A simpler unified analysis of budget perceptrons
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...
Ilya Sutskever
CORR
2011
Springer
202views Education» more  CORR 2011»
13 years 2 months ago
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
We analyze a class of estimators based on a convex relaxation for solving highdimensional matrix decomposition problems. The observations are the noisy realizations of the sum of ...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
ICCAD
2006
IEEE
152views Hardware» more  ICCAD 2006»
14 years 4 months ago
System-wide energy minimization for real-time tasks: lower bound and approximation
We present a dynamic voltage scaling (DVS) technique that minimizes system-wide energy consumption for both periodic and sporadic tasks. It is known that a system consists of proc...
Xiliang Zhong, Cheng-Zhong Xu
JOTA
2010
117views more  JOTA 2010»
13 years 6 months ago
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
We consider a distributed multi-agent network system where the goal is to minimize a sum of agent objective functions subject to a common set of constraints. For this problem, we p...
S. Sundhar Ram, Angelia Nedic, Venugopal V. Veerav...