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pro vyhledávání: '"Meister, Mona"'
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 while taking given safety constraints into account. For time-series modeli
Externí odkaz:
http://arxiv.org/abs/2402.06276
Learning the kernel parameters for Gaussian processes is often the computational bottleneck in applications such as online learning, Bayesian optimization, or active learning. Amortizing parameter inference over different datasets is a promising appr
Externí odkaz:
http://arxiv.org/abs/2306.09819
Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical dependencies exhibit nonlinear and nonstationary behavior. Machine learning
Externí odkaz:
http://arxiv.org/abs/2303.10022
Despite recent advances in automated machine learning, model selection is still a complex and computationally intensive process. For Gaussian processes (GPs), selecting the kernel is a crucial task, often done manually by the expert. Additionally, ev
Externí odkaz:
http://arxiv.org/abs/2210.11836
Publikováno v:
Proceedings of Machine Learning Research Volume 54: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 2017 (A. Singh and J. Zhu, eds.), pp. 1329-1337, 2017
Although support vector machines (SVMs) are theoretically well understood, their underlying optimization problem becomes very expensive, if, for example, hundreds of thousands of samples and a non-linear kernel are considered. Several approaches have
Externí odkaz:
http://arxiv.org/abs/1612.00374
Autor:
Schillinger, Mark *, Hartmann, Benjamin *, Skalecki, Patric **, Meister, Mona **, Nguyen-Tuong, Duy **, Nelles, Oliver ***
Publikováno v:
In IFAC PapersOnLine July 2017 50(1):5967-5972
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 while taking given safety constraints into account. For time-series modeli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ddcded4d57ac9f76146ff27966a202a
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