Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater expressiveness of this ...
Authors. Kurt Miller, Michael Jordan, Thomas Griffiths. Abstract. As the availability and importance of relational data -- such as the friendships ...
Feb 24, 2016 · In this paper, we present a max-margin learning method for such nonparametric latent feature relational models. Our approach attempts to unite ...
Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater expressiveness of this ...
Abstract. We present a max-margin nonparametric latent feature relational model, which u- nites the ideas of max-margin learning and.
Feb 1, 2017 · Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent feature models have been widely used.
This model allows us to capture sparse networks with latent structure. Existing network models focus either on latent structure—capturing the fact that each ...
This approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction, ...
Abstract. We present a max-margin nonparametric latent feature model, which unites the ideas of max-margin learning and Bayesian nonparametrics to discover ...
Dec 7, 2009 · Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater ...