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Found 3 papers, 2 papers with code
Adaptive Cholesky Gaussian Processes
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data.
Conjugate Gradients for Kernel Machines
Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
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