Prof. Dr.

Wrobel, Stefan

Filters

1 1
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    Wasserstein Dropout
    ( 2024)
    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 Novel Regression Loss for Non-Parametric Uncertainty Optimization
    ( 2021)
    Sicking, Joachim
    ;
    Akila, Maram
    ;
    Pintz, Maximilian
    ;
    Wirtz, Tim
    ;
    Fischer, Asja
    ;
    Wrobel, Stefan
    Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially for neural networks. One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice. However, it can underestimate the uncertainty. We propose a new objective, referred to as second-moment loss (SML), to address this issue. While the full network is encouraged to model the mean, the dropout networks are explicitly used to optimize the model variance. We intensively study the performance of the new objective on various UCI regression datasets. Comparing to the state-of-the-art of deep ensembles, SML leads to comparable prediction accuracies and uncertainty estimates while only requiring a single model. Under distribution shift, we observe moderate improvements. As a side result, we introduce an intuitive Wasserstein distance-based uncertainty measure that is non-saturating and thus allows to resolve quality differences between any two uncertainty estimates.