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Regularized Sparse Gaussian Processes
An issue faced by SGP, especially in latent variable models, is the inefficient learning of the inducing inputs, which leads to poor model prediction.
Hierarchical Hidden Markov Jump Processes for Cancer Screening Modeling
In this paper, we propose a piece-wise stationary transition matrix to explain the heterogeneity in time.
Nonstationary Multivariate Gaussian Processes for Electronic Health Records
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the correlations between the observed output, are all time dependent.
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