Florian Pfisterer

According to our database, Florian Pfisterer authored at least 34 papers between 2016 and 2024.

Collaborative distances:

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

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

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

Fairness Audits and Debiasing Using \pkg{mlr3fairness}.

Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl

R J., March, 2023

A geometric framework for outlier detection in high-dimensional data.

Moritz Herrmann, Florian Pfisterer, Fabian Scheipl

WIREs Data. Mining. Knowl. Discov., 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, Nadja Klein, Christian L. Müller

J. Stat. Softw., 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

CoRR, 2023

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives.

Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter

Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness.

Jan Simson, Florian Pfisterer, Christoph Kern

Proceedings of the HHAI 2023: Augmenting Human Intellect, 2023

What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness.

Jan Simson, Florian Pfisterer, Christoph Kern

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

2022

Democratizing machine learning: contributions in AutoML and fairness.

Florian Pfisterer

PhD thesis, 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

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

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

Flexible Group Fairness Metrics for Survival Analysis.

Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sebastian J. Vollmer

CoRR, 2022

A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models.

Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Multi-objective counterfactual fairness.

Susanne Dandl, Florian Pfisterer, Bernd Bischl

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Evaluating Domain Generalization for Survival Analysis in Clinical Studies.

Florian Pfisterer, Chris Harbron, Gunther Jansen, Tao Xu

Proceedings of the Conference on Health, Inference, and Learning, 2022

Tackling Neural Architecture Search With Quality Diversity Optimization.

Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas

Proceedings of the International Conference on Automated Machine Learning, 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

mcboost: Multi-Calibration Boosting for R.

Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl

J. Open Source Softw., 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

YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.

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

CoRR, 2021

Mutation is all you need.

Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl

CoRR, 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

Learning multiple defaults for machine learning algorithms.

Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Meta-learning for symbolic hyperparameter defaults.

Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020

Debiasing classifiers: is reality at variance with expectation?

Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer

CoRR, 2020

Neural Mixture Distributional Regression.

David Rügamer, Florian Pfisterer, Bernd Bischl

CoRR, 2020

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

Benchmarking time series classification - Functional data vs machine learning approaches.

Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl

CoRR, 2019

Towards Human Centered AutoML.

Florian Pfisterer, Janek Thomas, Bernd Bischl

CoRR, 2019

Multi-Objective Automatic Machine Learning with AutoxgboostMC.

Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl

CoRR, 2019

High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions.

Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl

Proceedings of the Intelligent Systems and Applications, 2019

2016

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