Florian Pfisterer

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 44

Top Topics

Outlier Detection

Design Decisions

Neural Architecture

Machine Learning

Top Venues

GECCO Companion

J. Open Source Softw.

Publications

Jan Simson, Florian Pfisterer, Christoph Kern
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions. FAccT (2024)
Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. 79 (2024)
Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR (2023)
Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl
Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. 15 (1) (2023)
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
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105 (2) (2023)
Jan Simson, Florian Pfisterer, Christoph Kern
What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. EWAF (2023)
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
A geometric framework for outlier detection in high-dimensional data. WIREs Data. Mining. Knowl. Discov. 13 (3) (2023)
Jan Simson, Florian Pfisterer, Christoph Kern
Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. CoRR (2023)
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
Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. ACM Trans. Evol. Learn. Optim. 3 (4) (2023)
Jan Simson, Florian Pfisterer, Christoph Kern
Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. HHAI (2023)
Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA (2023)
Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR (2022)
Susanne Dandl, Florian Pfisterer, Bernd Bischl
Multi-objective counterfactual fairness. GECCO Companion (2022)
Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Comput. Stat. 37 (5) (2022)
Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas
Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML (2022)
Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. 26 (6) (2022)
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
A geometric framework for outlier detection in high-dimensional data. CoRR (2022)
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML (2022)
Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. GECCO Companion (2022)
Florian Pfisterer, Chris Harbron, Gunther Jansen, Tao Xu
Evaluating Domain Generalization for Survival Analysis in Clinical Studies. CHIL (2022)
Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas
Tackling Neural Architecture Search With Quality Diversity Optimization. CoRR (2022)
Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sebastian J. Vollmer
Flexible Group Fairness Metrics for Survival Analysis. CoRR (2022)
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
Multi-Objective Hyperparameter Optimization - An Overview. CoRR (2022)
Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. CoRR (2022)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR (2021)
Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. CoRR (2021)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-learning for symbolic hyperparameter defaults. GECCO Companion (2021)
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
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR (2021)
Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl
mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. 22 (2021)
Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. CoRR (2021)
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. CoRR (2021)
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
Learning multiple defaults for machine learning algorithms. GECCO Companion (2021)
Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl
mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. 6 (64) (2021)
Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl
Mutation is all you need. CoRR (2021)
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer
Debiasing classifiers: is reality at variance with expectation? CoRR (2020)
David Rügamer, Florian Pfisterer, Bernd Bischl
Neural Mixture Distributional Regression. CoRR (2020)
Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl
Benchmarking time series classification - Functional data vs machine learning approaches. CoRR (2019)
Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions. CoRR (2019)
Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl
High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. IntelliSys (1) (2019)
Florian Pfisterer, Janek Thomas, Bernd Bischl
Towards Human Centered AutoML. CoRR (2019)
Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl
Multi-Objective Automatic Machine Learning with AutoxgboostMC. CoRR (2019)
Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl
mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4 (44) (2019)
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
Learning Multiple Defaults for Machine Learning Algorithms. CoRR (2018)
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
mlr Tutorial. CoRR (2016)