Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Steland, Ansgar"'
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In ma
Externí odkaz:
http://arxiv.org/abs/2402.18260
Autor:
Steland, Ansgar
Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a data set has been collected under normal conditions. The app
Externí odkaz:
http://arxiv.org/abs/2310.08150
Autor:
Steland, Ansgar
Sequential monitoring of high-dimensional nonlinear time series is studied for a projection of the second-moment matrix, a problem interesting in its own right and specifically arising in finance and deep learning. Open-end as well as closed-end moni
Externí odkaz:
http://arxiv.org/abs/2302.07198
Autor:
Mies, Fabian, Steland, Ansgar
Analyzing large samples of high-dimensional data under dependence is a challenging statistical problem as long time series may have change points, most importantly in the mean and the marginal covariances, for which one needs valid tests. Inference f
Externí odkaz:
http://arxiv.org/abs/2211.02368
Autor:
Mies, Fabian, Steland, Ansgar
Gaussian couplings of partial sum processes are derived for the high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of independent random vectors and subsequently extended to nonstationary time series. Our inequalities depend exp
Externí odkaz:
http://arxiv.org/abs/2203.03237
Autor:
Steland, Ansgar, Pieters, Bart E.
Randomized artificial neural networks such as extreme learning machines provide an attractive and efficient method for supervised learning under limited computing ressources and green machine learning. This especially applies when equipping mobile de
Externí odkaz:
http://arxiv.org/abs/2101.01990
Publikováno v:
International Journal of Quality & Reliability Management, 2022, Vol. 40, Issue 7, pp. 1597-1620.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJQRM-11-2021-0408