4 citations · 9 across the 11 of their papers we have counts for
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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…
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels
Matthias Bitzer, Mona Meister, Christoph Zimmer
Learning the kernel parameters for Gaussian processes is often the computational bottleneck in applications such as online learning, Bayesian optimization, or active learning. Amor…
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems
Matthias Bitzer, Mona Meister, Christoph Zimmer
Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical depen…
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
Matthias Bitzer, Mona Meister, Christoph Zimmer
Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel…
Safe Active Learning for Multi-Output Gaussian Processes
Cen-You Li, Barbara Rakitsch, Christoph Zimmer
Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a promising tool for model…
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…