5 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Spatial Decompositions for Large Scale SVMs
Philipp Thomann, Ingrid Blaschzyk, Mona Meister +1
Although support vector machines (SVMs) are theoretically well understood, their underlying optimization problem becomes very expensive, if, for example, hundreds of thousands of s…
Optimal Learning Rates for Localized SVMs
Mona Eberts, Ingo Steinwart
One of the limiting factors of using support vector machines (SVMs) in large scale applications are their super-linear computational requirements in terms of the number of training…