This paper describes a new probabilistic generative model for noisy, sparse, and heterogeneous relational data called the hierarchical infinite relational model (HIRM). Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.

Hierarchical Infinite Relational Model

**Session Chair**: Ashish Sabharwal
 
 
**ID // Presenter // Live Discussant** 
405 // Feras Saad // Saquib Hashmi 
713 // Aaron Schein // Jaivardhan Kapoor 
770 Nicholas Monath // Saptarashmi Bandyopadhyay 
841 Boyan Beronov // Samuel Hakansson

Dimensionality Reduction

technical paper

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

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UAI 2021

The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. It is supported by the [Association for Uncertainty in Artificial Intelligence (AUAI).](https://www.auai.org/)

Feras Saad

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Hierarchical Infinite Relational Model

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