Selected Publications

Importance Weighted Variational Inference for Deep Gaussian Processes

Gaussian Process Conditional Density Estimation

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Recent Publications

Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its …

Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential …

Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution …

The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely …

We propose a new method of variational inference for the Deep Gaussian Process model that is highly scalable and works well in …