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UAI
1997
13 years 10 months ago
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expresse...
Fabio Gagliardi Cozman
SAS
2005
Springer
132views Formal Methods» more  SAS 2005»
14 years 2 months ago
Generation of Basic Semi-algebraic Invariants Using Convex Polyhedra
A technique for generating invariant polynomial inequalities of bounded degree is presented using the abstract interpretation framework. It is based on overapproximating basic semi...
Roberto Bagnara, Enric Rodríguez-Carbonell,...
ICASSP
2009
IEEE
14 years 16 days ago
Design of robust superdirective beamformers as a convex optimization problem
Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative t...
Edwin Mabande, Adrian Schad, Walter Kellermann
AAAI
2006
13 years 10 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
ICML
2006
IEEE
14 years 9 months ago
Discriminative unsupervised learning of structured predictors
We present a new unsupervised algorithm for training structured predictors that is discriminative, convex, and avoids the use of EM. The idea is to formulate an unsupervised versi...
Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dal...