Structured factor copula models: Theory, inference and computation

Factor copula models for item response data.

Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimen...

Learning and Inference in Structured Prediction Models

This tutorial targets AI researchers who are interested in designing and applying structured prediction models to problems with interdependent output variables. The tutorial will introduce the problem of structured prediction exemplifying it in multiple AI problems, and then cover recent developments in efficient inference and learning methods in discriminative structured models and outline fur...

Copula variational inference

We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of distributions used inmean-field and structured approximations. Copulas model the dependency that is not captured by the original variational distribution, and thus the augmented variational family guarantees better approximations to the poste...

Variational Gaussian Copula Inference

We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaussian copula family is able to preserve multivariate posterior dependence, and the nonpa...

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