Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Multi-objective hyperparameter tuning and feature selection using filter ensembles. - Martin Binder; Julia Moosbauer; Janek Thomas; Bernd Bischl (GECCO, 2020)

Train, Learn, Expand, Repeat. - Abhijeet Parida; Aadhithya Sankar; Rami Eisawy; Tom Finck; Benedikt Wiestler; Franz Pfister; Julia Moosbauer (CoRR, 2020)

Explaining Hyperparameter Optimization via Partial Dependence Plots. - Julia Moosbauer; Julia Herbinger; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (NeurIPS, 2021)

YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (CoRR, 2021)

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (AutoML, 2022)

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. - Julia Moosbauer; Giuseppe Casalicchio; Marius Lindauer; Bernd Bischl (CoRR, 2022)

Multi-Objective Hyperparameter Optimization - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (CoRR, 2022)

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. - Julia Moosbauer; Martin Binder; Lennart Schneider; Florian Pfisterer; Marc Becker; Michel Lang; Lars Kotthoff; Bernd Bischl (IEEE Trans. Evol. Comput., 2022)

RAISE - Radiology AI Safety, an End-to-end lifecycle approach. - M. Jorge Cardoso; Julia Moosbauer; Tessa Sundaram Cook; Barbaros Selnur Erdal; Brad W. Genereaux; Vikash Gupta; Bennett A. Landman; Tiarna Lee; Parashkev Nachev; Elanchezhian Somasundaram; Ronald M. Summers; Khaled Younis; et al. (CoRR, 2023)

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. - Christian A. Scholbeck; Julia Moosbauer; Giuseppe Casalicchio; Hoshin Gupta; Bernd Bischl; Christian Heumann (CoRR, 2023)

Towards explainable automated machine learning. - Julia Moosbauer (2023)

Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (ACM Trans. Evol. Learn. Optim., 2023)

Position: A Call to Action for a Human-Centered AutoML Paradigm. - Marius Lindauer; Florian Karl; Anne Klier; Julia Moosbauer; Alexander Tornede; Andreas Müller 024; Frank Hutter; Matthias Feurer; Bernd Bischl (ICML, 2024)