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Found 9 papers, 6 papers with code
Influenza Forecasting Framework based on Gaussian Processes
The seasonal epidemic of influenza costs thousands of lives each year in the US.
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space.
Global Safe Sequential Learning via Efficient Knowledge Transfer
As transferable source knowledge is often available in safety critical experiments, we propose to consider transfer safe sequential learning to accelerate the learning of safety.
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.
Safe Active Learning for Multi-Output Gaussian Processes
Multi-output regression problems are commonly encountered in science and engineering.
Active Learning in Gaussian Process State Space Model
We investigate active learning in Gaussian Process state-space models (GPSSM).
