Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
Penalized blind kriging in computer experiments
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A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.
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Theta-regularized Kriging: Modelling and Algorithms
Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
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Active Learning for Manifold Gaussian Process Regression
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.