Julia Moosbauer

According to our database, Julia Moosbauer authored at least 14 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Position: A Call to Action for a Human-Centered AutoML Paradigm.

Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, Bernd Bischl

ACM Trans. Evol. Learn. Optim., December, 2023

Towards explainable automated machine learning.

Julia Moosbauer

PhD thesis, 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

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, Sébastien Ourselin, Franz Pfister

CoRR, 2023

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

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, Bernd Bischl

CoRR, 2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution.

Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

CoRR, 2022

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization.

Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl

Proceedings of the International Conference on Automated Machine Learning, 2022

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

Explaining Hyperparameter Optimization via Partial Dependence Plots.

Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Train, Learn, Expand, Repeat.

Abhijeet Parida, Aadhithya Sankar, Rami Eisawy, Tom Finck, Benedikt Wiestler, Franz Pfister, Julia Moosbauer

CoRR, 2020

Multi-objective hyperparameter tuning and feature selection using filter ensembles.

Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

Model-Agnostic Approaches to Multi-Objective Simultaneous Hyperparameter Tuning and Feature Selection.

Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl

CoRR, 2019