mlr Tutorial. - Julia Schiffner; Bernd Bischl; Michel Lang; Jakob Richter; Zachary M. Jones; Philipp Probst; Florian Pfisterer; Mason Gallo; Dominik Kirchhoff; Tobias Kühn; Janek Thomas; Lars Kotthoff (CoRR, 2016)

High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. - Xudong Sun 002; Andrea Bommert; Florian Pfisterer; Jörg Rahnenführer; Michel Lang; Bernd Bischl (IntelliSys (1), 2019)

Multi-Objective Automatic Machine Learning with AutoxgboostMC. - Florian Pfisterer; Stefan Coors; Janek Thomas; Bernd Bischl (CoRR, 2019)

Towards Human Centered AutoML. - Florian Pfisterer; Janek Thomas; Bernd Bischl (CoRR, 2019)

Benchmarking time series classification - Functional data vs machine learning approaches. - Florian Pfisterer; Laura Beggel; Xudong Sun 002; Fabian Scheipl; Bernd Bischl (CoRR, 2019)

mlr3: A modern object-oriented machine learning framework in R. - Michel Lang; Martin Binder; Jakob Richter; Patrick Schratz; Florian Pfisterer; Stefan Coors; Quay Au; Giuseppe Casalicchio; Lars Kotthoff; Bernd Bischl (J. Open Source Softw., 2019)

mlr3: A modern object-oriented machine learning framework in R. - Michel Lang; Martin Binder; Jakob Richter; Patrick Schratz; Florian Pfisterer; Stefan Coors; Quay Au; Giuseppe Casalicchio; Lars Kotthoff; Bernd Bischl (Zenodo, 2019)

Neural Mixture Distributional Regression. - David Rügamer; Florian Pfisterer; Bernd Bischl (CoRR, 2020)

Debiasing classifiers: is reality at variance with expectation? - Ashrya Agrawal; Florian Pfisterer; Bernd Bischl; Jiahao Chen 001; Srijan Sood; Sameena Shah; Francois Buet-Golfouse; Bilal A. Mateen; Sebastian J. Vollmer (CoRR, 2020)

Meta-learning for symbolic hyperparameter defaults. - Pieter Gijsbers; Florian Pfisterer; Jan N. van Rijn; Bernd Bischl; Joaquin Vanschoren (GECCO Companion, 2021)

Learning multiple defaults for machine learning algorithms. - Florian Pfisterer; Jan N. van Rijn; Philipp Probst; Andreas C. Müller 002; Bernd Bischl (GECCO Companion, 2021)

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - David Rügamer; Ruolin Shen; Christina Bukas; Lisa Barros de Andrade e Sousa; Dominik Thalmeier; Nadja Klein; Chris Kolb; Florian Pfisterer; Philipp Kopper; Bernd Bischl; Christian L. Müller (CoRR, 2021)

Mutation is all you need. - Lennart Schneider; Florian Pfisterer; Martin Binder; Bernd Bischl (CoRR, 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)

mlr3pipelines - Flexible Machine Learning Pipelines in R. - Martin Binder; Florian Pfisterer; Michel Lang; Lennart Schneider; Lars Kotthoff; Bernd Bischl (J. Mach. Learn. Res., 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Matthew Sun; Michael P. Kim; Bernd Bischl (J. Open Source Softw., 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Mathew Sun; Michael P. Kim; Bernd Bischl (Zenodo, 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)

Tackling Neural Architecture Search With Quality Diversity Optimization. - Lennart Schneider; Florian Pfisterer; Paul Kent; Jürgen Branke; Bernd Bischl; Janek Thomas (AutoML, 2022)

Evaluating Domain Generalization for Survival Analysis in Clinical Studies. - Florian Pfisterer; Chris Harbron; Gunther Jansen; Tao Xu (CHIL, 2022)

Multi-objective counterfactual fairness. - Susanne Dandl; Florian Pfisterer; Bernd Bischl (GECCO Companion, 2022)

A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. - Lennart Schneider; Florian Pfisterer; Janek Thomas; Bernd Bischl (GECCO Companion, 2022)

Flexible Group Fairness Metrics for Survival Analysis. - Raphael Sonabend; Florian Pfisterer; Alan Mishler; Moritz Schauer; Lukas Burk; Sebastian J. Vollmer (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)

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. - Florian Pargent; Florian Pfisterer; Janek Thomas; Bernd Bischl (Comput. Stat., 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)

Democratizing machine learning: contributions in AutoML and fairness. - Florian Pfisterer (2022)

What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. - Jan Simson; Florian Pfisterer; Christoph Kern 001 (EWAF, 2023)

Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. - Jan Simson; Florian Pfisterer; Christoph Kern 001 (HHAI, 2023)

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. - Matthias Feurer; Katharina Eggensperger; Edward Bergman; Florian Pfisterer; Bernd Bischl; Frank Hutter (IDA, 2023)

Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. - Jan Simson; Florian Pfisterer; Christoph Kern 001 (CoRR, 2023)

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - David Rügamer; Chris Kolb; Cornelius Fritz; Florian Pfisterer; Philipp Kopper; Bernd Bischl; Ruolin Shen; Christina Bukas; Lisa Barros de Andrade e Sousa; Dominik Thalmeier; Philipp F. M. Baumann; Lucas Kook; et al. (J. Stat. Softw., 2023)

A geometric framework for outlier detection in high-dimensional data. - Moritz Herrmann; Florian Pfisterer; Fabian Scheipl (WIREs Data. Mining. Knowl. Discov., 2023)

Fairness Audits and Debiasing Using pkg{mlr3fairness}. - Florian Pfisterer; Siyi Wei; Sebastian J. Vollmer; Michel Lang; Bernd Bischl (R J., 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)

One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions. - Jan Simson; Florian Pfisterer; Christoph Kern 001 (FAccT, 2024)

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. - Hilde J. P. Weerts; Florian Pfisterer; Matthias Feurer; Katharina Eggensperger; Edward Bergman; Noor H. Awad; Joaquin Vanschoren; Mykola Pechenizkiy; Bernd Bischl; Frank Hutter (J. Artif. Intell. Res., 2024)