Fast Inference and Learning on Hybrid Relational Probabilistic Graphical Models




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Probabilistic Relational Models (PRMs) combine the power of Probabilistic Graphical Mod- els modeling structural data and the capability of First-order Logic representing relationship on class level. This type of model has shown success on modeling large data, especially en- terprise data that are stored in relational databases. Efficient reasoning and model learning of PRMs are critical problems for applying them to large-scale tasks. With recent advances in lifted inference, symmetries in the PRMs can exploited to perform efficient reasoning and learning. However, although most real-world applications involve both discrete and continu- ous (hybrid) features, most existing lifted inference methods have been restricted to discrete models or continuous models with systematic assumptions, limiting their applications and ease of use. To extend the applicability of PRMs, we need to design lifted inference methods and model learning algorithms that are suitable for hybrid data. In this work, we develop approximate lifted inference schemes based on particle sampling and variational inference, which have the ability to perform inference on arbitrary hybrid domain Markov Random Field (MRF) models. We also introduce Relational Neural Markov Random Field (RN-MRF) models that allow handling of complex relational features with the help of both neural potential functions and expert defined relational rules. Finally, we propose a maximum pseudo-likelihood estimation-based learning algorithm with importance sampling for training the RN-MRF models. The key advantage of our inference and learning approach is that they make minimal data distributional assumptions and have the flexibility to be applied to various real-world application. We demonstrate empirically that our inference methods and proposed learning model are efficient and outperforming existing approaches in a variety of settings.



Computer Science, Artificial Intelligence