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12/21/2020
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Choosing a proper set of kernel functions is an important problem in lea...
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10/19/2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Recent advances in Deep Gaussian Processes (DGPs) show the potential to ...
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05/30/2019
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
In the analysis of sequential data, the detection of abrupt changes is i...
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03/28/2017
Discovering Explainable Latent Covariance Structure for Multiple Time Series
Analyzing time series data is important to predict future events and cha...
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07/04/2016
Automatic Generation of Probabilistic Programming from Time Series Data
Probabilistic programming languages represent complex data with intermin...
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11/26/2015
