3,347 research outputs found
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
Stochastic Discriminative EM
Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for
discriminative training of probabilistic generative models belonging to the
exponential family. In this work, we introduce and justify this algorithm as a
stochastic natural gradient descent method, i.e. a method which accounts for
the information geometry in the parameter space of the statistical model. We
show how this learning algorithm can be used to train probabilistic generative
models by minimizing different discriminative loss functions, such as the
negative conditional log-likelihood and the Hinge loss. The resulting models
trained by sdEM are always generative (i.e. they define a joint probability
distribution) and, in consequence, allows to deal with missing data and latent
variables in a principled way either when being learned or when making
predictions. The performance of this method is illustrated by several text
classification problems for which a multinomial naive Bayes and a latent
Dirichlet allocation based classifier are learned using different
discriminative loss functions.Comment: UAI 2014 paper + Supplementary Material. In Proceedings of the
Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI 2014),
edited by Nevin L. Zhang and Jian Tian. AUAI Pres
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