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NIPS
2007
13 years 6 months ago
Convex Relaxations of Latent Variable Training
We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while eliminating local minima. First, a cautionary resu...
Yuhong Guo, Dale Schuurmans
CVPR
2012
IEEE
11 years 7 months ago
What is optimized in tight convex relaxations for multi-label problems?
In this work we present a unified view on Markov random fields and recently proposed continuous tight convex relaxations for multi-label assignment in the image plane. These rel...
Christopher Zach, Christian Hane, Marc Pollefeys
AUTOMATICA
2008
167views more  AUTOMATICA 2008»
13 years 5 months ago
Stability and robustness analysis of nonlinear systems via contraction metrics and SOS programming
A wide variety of stability and performance questions about linear dynamical systems can be reformulated as convex optimization problems involving linear matrix inequalities (LMIs...
Erin M. Aylward, Pablo A. Parrilo, Jean-Jacques E....
OL
2011
177views Neural Networks» more  OL 2011»
12 years 8 months ago
Exploiting vector space properties to strengthen the relaxation of bilinear programs arising in the global optimization of proce
In this paper we present a methodology for finding tight convex relaxations for a special set of quadratic constraints given by bilinear and linear terms that frequently arise in ...
Juan P. Ruiz, Ignacio E. Grossmann
CORR
2007
Springer
130views Education» more  CORR 2007»
13 years 5 months ago
Lagrangian Relaxation for MAP Estimation in Graphical Models
Abstract— We develop a general framework for MAP estimation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to reformulate an in...
Jason K. Johnson, Dmitry M. Malioutov, Alan S. Wil...