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  1. Article

    Open Access

    Learning de-biased regression trees and forests from complex samples

    Regression trees and forests are widely used due to their flexibility and predictive accuracy. Whereas typical tree induction assumes independently identically distributed (i.i.d.) data, in many applications t...

    Malte Nalenz, Julian Rodemann, Thomas Augustin in Machine Learning (2024)

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  2. No Access

    Chapter and Conference Paper

    Levelwise Data Disambiguation by Cautious Superset Classification

    Drawing conclusions from set-valued data calls for a trade-off between caution and precision. In this paper, we propose a way to construct a hierarchical family of subsets within set-valued categorical observa...

    Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier… in Scalable Uncertainty Management (2022)

  3. No Access

    Chapter and Conference Paper

    Accounting for Gaussian Process Imprecision in Bayesian Optimization

    Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we propose Prior-mean-RObust Baye...

    Julian Rodemann, Thomas Augustin in Integrated Uncertainty in Knowledge Modell… (2022)