Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Comparison of semiparametric and parametric methods for estimating copulas
Computational Statistics & Data Analysis
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Proceedings of the 24th international conference on Machine learning
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
The Journal of Machine Learning Research
Semi-Supervised Classification of Network Data Using Very Few Labels
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Understanding Propagation Error and Its Effect on Collective Classification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Low-Rank Variance Approximation in GMRF Models: Single and Multiscale Approaches
IEEE Transactions on Signal Processing - Part I
A few good predictions: selective node labeling in a social network
Proceedings of the 7th ACM international conference on Web search and data mining
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The popularity of online social networks and social media has increased the amount of linked data available in Web domains. Relational and Gaussian Markov networks have both been applied successfully for classification in these relational settings. However, since Gaussian Markov networks model joint distributions over continuous label space, it is difficult to use them to reason about uncertainty in discrete labels. On the other hand, relational Markov networks model probability distributions over discrete label space, but since they condition on the graph structure, the marginal probability for an instance will vary based on the structure of the subnetwork observed around the instance. This implies that the marginals will not be identical across instances and can sometimes result in poor prediction performance. In this work, we propose a novel latent relational model based on copulas which allows use to make predictions in a discrete label space while ensuring identical marginals and at the same time incorporating some desirable properties of modeling relational dependencies in a continuous space. While copulas have recently been used for descriptive modeling, they have not been used for collective classification in large scale network data and the associated conditional inference problem has not been considered before. We develop an approximate inference algorithm, and demonstrate empirically that our proposed Copula Latent Markov Network models based on approximate inference outperform a number of competing relational classification models over a range of real-world relational classification tasks.