
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GPLVM) provides a flexible ...
09/08/2014 ∙ by Andreas C. Damianou, et al. ∙ 0 ∙ shareread it

GaussianDirichlet Posterior Dominance in Sequential Learning
We consider the problem of sequential learning from categorical observat...
02/14/2017 ∙ by Ian Osband, et al. ∙ 0 ∙ shareread it

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multip...
05/27/2017 ∙ by Zhenwen Dai, et al. ∙ 0 ∙ shareread it

Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of blackbox problems that comprise both continuo...
06/20/2019 ∙ by Binxin Ru, et al. ∙ 5 ∙ shareread it

Unsupervised and interpretable scene discovery with DiscreteAttendInferRepeat
In this work we present Discrete Attend Infer Repeat (DiscreteAIR), a R...
03/14/2019 ∙ by Duo Wang, et al. ∙ 0 ∙ shareread it

Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models  a Gentle Tutorial
In this tutorial we explain the inference procedures developed for the s...
02/06/2014 ∙ by Yarin Gal, et al. ∙ 0 ∙ shareread it

Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in c...
02/12/2018 ∙ by Francisco J. R. Ruiz, et al. ∙ 0 ∙ shareread it
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a nonlinear transformation of a continuous latent space. Nonlinearity captures multimodality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.
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