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stat.ML2025
Beyond State Space Representation: A General Theory for Kernel Packets
Liang Ding, Rui Tuo, Lu Zhou
Gaussian process (GP) regression provides a flexible, nonparametric framework for probabilistic modeling, yet remains computationally demanding in large-scale applications. For one…
stat.ML2024
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
Lu Zou, Liang Ding
Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the con…