8 citations · 9 across the 3 of their papers we have counts for
5 papers
Batch Active Learning in Gaussian Process Regression using Derivatives
Hon Sum Alec Yu, Christoph Zimmer, Duy Nguyen-Tuong
We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for…
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong
Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models whil…
Active Learning in Gaussian Process State Space Model
Hon Sum Alec Yu, Dingling Yao, Christoph Zimmer +2
We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that…
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg +4
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) p…
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco +2
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in pr…