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From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained …
Wenyuan Zhao
,
Haoyuan Chen
,
Tie Liu
,
Rui Tuo
,
Chao Tian
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Aggregation Models with Optimal Weights for Distributed Gaussian Processes
GP models have received increasingly attentions in recent years due to their super prediction accuracy and modeling flexibility. To …
Haoyuan Chen
,
Rui Tuo
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Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
Gaussian processes (GPs) are widely used in non-parametric Bayesian modeling, and play an important role in various statistical and …
Haoyuan Chen
,
Rui Tuo
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Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather …
Haoyuan Chen
,
Emil Constantinescu
,
Vishwas Rao
,
Cristiana Stan
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Poster
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Matérn correlations whose smoothness …
Haoyuan Chen
,
Liang Ding
,
Rui Tuo
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