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ICML
2009
IEEE
14 years 11 months ago
Convex variational Bayesian inference for large scale generalized linear models
We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
Hannes Nickisch, Matthias W. Seeger
SCALESPACE
2009
Springer
14 years 4 months ago
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We sugg...
Jan Lellmann, Jörg H. Kappes, Jing Yuan, Flor...
CDC
2009
IEEE
149views Control Systems» more  CDC 2009»
14 years 2 months ago
Solving large-scale linear circuit problems via convex optimization
Abstract— A broad class of problems in circuits, electromagnetics, and optics can be expressed as finding some parameters of a linear system with a specific type. This paper is...
Javad Lavaei, Aydin Babakhani, Ali Hajimiri, John ...
AAAI
2006
13 years 11 months ago
Robust Support Vector Machine Training via Convex Outlier Ablation
One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitig...
Linli Xu, Koby Crammer, Dale Schuurmans
ICASSP
2011
IEEE
13 years 1 months ago
Convex relaxation approaches to maximum likelihood DOA estimation in ULA's and UCA's with unknown mutual coupling
Direction of arrival (DOA) estimation using sensor array superresolution techniques are known to suffer from array modeling errors including array element displacements, mutual co...
Kehu Yang, Shu Cai, Zhi-Quan Luo