98 citations · 204 across the 31 of their papers we have counts for
34 papers
Context-Adaptive Thresholding for Conditionally Representative Monitoring and Classification
Ansgar Steland
Commonly, classifiers and monitoring procedures are trained from labeled data by optimizing an objective such as the misclassification rate. This may lead to unrepresentative condi…
Adaptive Thresholds for Monitoring and Screening in Imbalanced Samples: Optimality and Boosting Sensitivity
Ansgar Steland
Suppose (standardized) measurements or statistics are monitored to raise an alarm when a threshold is exceeded. Often, the underlying population is heterogenous with respect to imp…
On the Weak Convergence of the Function-Indexed Sequential Empirical Process and its Smoothed Analogue under Nonstationarity
Florian Alexander Scholze, Ansgar Steland
We study the sequential empirical process indexed by general function classes and its smoothed set-indexed analogue. Sufficient conditions for asymptotic equicontinuity are provide…
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland +2
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their cali…
On Extreme Value Asymptotics of Projected Sample Covariances in High Dimensions with Applications in Finance and Convolutional Networks
Ansgar Steland
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 hypothes…
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?
Ansgar Steland
Training deep learning neural networks often requires massive amounts of computational ressources. We propose to sequentially monitor network predictions to trigger retraining only…