Automatic model construction with Gaussian processes

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How researchers see this paper

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Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “Matérn 5/2 kernel is widely used because of its flexibil- ity (Duvenaud, 2014) with learnable length scale parameters are common default kernel functions for many applications (e.g., Spearmint and MOE).”
Gaussian process regression for forecasting battery state of health (2017, neutral): “4 (using equations 2.17 and 2.18 from [28]).”
Morphable Face Models - An Open Framework (2017, methodology): “Then the following rules can be used to generate new positive semi-definite kernels, which is well described in [20]:”
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs (2018, neutral): “ approaches, the kernel functional form is thus restricted to simple kernels with few hyperparameters, whose behaviors have already been investigated by the community, e.g., in the kernel cookbook by Duvenaud (2014). Once a reasonable kernel is chosen, it is necessary to t the hyperparameter and dep”
Convolutional Gaussian Processes (2017, methodology): “Other methods, such as EP/FITC [14, 20, 21, 22], can be seen as approximate models that do not share this property, leading to behaviour that would not be expected from the model that is to be approximated [23].”