Prof. Dr.

Wrobel, Stefan

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  • Publication
    Wasserstein Dropout
    ( 2022-09-08)
    Sicking, Joachim
    ;
    Akila, Maram
    ;
    Pintz, Maximilian Alexander
    ;
    Wirtz, Tim
    ;
    Wrobel, Stefan
    ;
    Fischer, Asja
    Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved. State-of-the-art approaches to estimate neural uncertainties are often hybrid, combining parametric models with explicit or implicit (dropout-based) ensembling. We take another pathway and propose a novel approach to uncertainty quantification for regression tasks, Wasserstein dropout, that is purely non-parametric. Technically, it captures aleatoric uncertainty by means of dropout-based sub-network distributions. This is accomplished by a new objective which minimizes the Wasserstein distance between the label distribution and the model distribution. An extensive empirical analysis shows that Wasserstein dropout outperforms state-of-the-art methods, on vanilla test data as well as under distributional shift in terms of producing more accurate and stable uncertainty estimates.
  • Publication
    A theoretical model for pattern discovery in visual analytics
    (Elsevier B.V., 2021-01-21)
    Andrienko, Natalia
    ;
    Andrienko, Gennady
    ;
    Miksch, Silvia
    ;
    Schumann, Heidrun
    ;
    Wrobel, Stefan
    The word 'pattern' frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.