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Found 5 papers, 3 papers with code
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Learning time-series models is useful for many applications, such as simulation and forecasting.
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels
We propose amortizing kernel parameter inference over a complete kernel-structure-family rather than a fixed kernel structure.
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems
Machine learning methods that are used to produce the surrogate model should therefore address these problems by providing a scheme to keep the number of queries small, e. g. by using active learning and be able to capture the nonlinear and nonstationary properties of the system.
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process.
Spatial Decompositions for Large Scale SVMs
Our contributions are two fold: On the theoretical side we establish an oracle inequality for the overall learning method using the hinge loss, and show that the resulting rates match those known for SVMs solving the complete optimization problem with Gaussian kernels.
