Hochschulschrift(en), die von Bischl, Bernd begutachtet wurde(n)

Dorigatti, Emilio (2024): Cancer immunotherapy design and analysis through discrete optimization, positive-unlabeled learning, and semi-structured regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Röck, Katharina (2024): Stochastic processes as surrogate models for dynamical systems in magnetic confinement fusion. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Au, Jiew-Quay (2024): Challenges in machine learning for predicting psychological attributes from smartphone data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Goschenhofer, Jann (2023): Reducing the effort for data annotation: contributions to weakly supervised deep learning. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Dandl, Susanne (2023): Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Herbinger, Julia (2023): On grouping and partitioning approaches in interpretable machine learning. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

König, Gunnar (2023): If interpretability is the answer, what is the question?: a causal perspective. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Ott, Felix (2023): Representation learning for domain adaptation and cross-modal retrieval: in the context of online handwriting recognition and visual self-localization. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Moosbauer, Julia (2023): Towards explainable automated machine learning. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Schalk, Daniel (2023): Modern approaches for component-wise boosting: Automation, efficiency, and distributed computing with application to the medical domain. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Pfisterer, Florian (2022): Democratizing machine learning: contributions in AutoML and fairness. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Molnar, Christoph (2022): Model-agnostic interpretable machine learning. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Sun, Xudong (2021): Machine learning model selection with multi-objective Bayesian optimization and reinforcement learning: case studies on functional data analysis, pipeline tuning and shifted distribution. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Thomas, Janek (2019): Gradient boosting in automatic machine learning: feature selection and hyperparameter optimization. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Casalicchio, Giuseppe (2019): On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik