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Found 5 papers, 1 papers with code
Active Learning in Gaussian Process State Space Model
We investigate active learning in Gaussian Process state-space models (GPSSM).
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Learning time-series models is useful for many applications, such as simulation and forecasting.
Probabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications.
Local Gaussian Process Regression for Real Time Online Model Learning
Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP).
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