David J. Nott

Marginally Calibrated Deep Distributional Regression

2020 · Journal of Computational and Graphical Statistics

Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to …

Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models

2021 · arXiv (Cornell University)

Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure for mixture components. There are several recent extensions of mixture of …