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Florian Pfisterer
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2020 – today
- 2024
- [j10]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: 639-677 (2024) - [c12]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: 1305-1320 - 2023
- [j9]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) - [j8]Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl:
Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. 15(1): 234-253 (2023) - [j7]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): 16:1-16:50 (2023) - [j6]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. WIREs Data. Mining. Knowl. Discov. 13(3) (2023) - [c11]Jan Simson, Florian Pfisterer, Christoph Kern:
What If? Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. EWAF 2023 - [c10]Jan Simson, Florian Pfisterer, Christoph Kern:
Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness. HHAI 2023: 382-384 - [c9]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA 2023: 130-142 - [i22]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 abs/2303.08485 (2023) - [i21]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 abs/2308.16681 (2023) - 2022
- [b1]Florian Pfisterer:
Democratizing machine learning: contributions in AutoML and fairness. Ludwig Maximilian University of Munich, Germany, 2022 - [j5]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): 2671-2692 (2022) - [j4]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): 1336-1350 (2022) - [c8]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML 2022: 3/1-39 - [c7]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML 2022: 9/1-30 - [c6]Florian Pfisterer, Chris Harbron, Gunther Jansen, Tao Xu:
Evaluating Domain Generalization for Survival Analysis in Clinical Studies. CHIL 2022: 32-47 - [c5]Susanne Dandl, Florian Pfisterer, Bernd Bischl:
Multi-objective counterfactual fairness. GECCO Companion 2022: 328-331 - [c4]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: 2136-2142 - [i20]Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl:
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. CoRR abs/2204.14061 (2022) - [i19]Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sebastian J. Vollmer:
Flexible Group Fairness Metrics for Survival Analysis. CoRR abs/2206.03256 (2022) - [i18]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 abs/2206.07438 (2022) - [i17]Moritz Herrmann, Florian Pfisterer, Fabian Scheipl:
A geometric framework for outlier detection in high-dimensional data. CoRR abs/2207.00367 (2022) - [i16]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. CoRR abs/2208.00204 (2022) - [i15]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR abs/2212.04183 (2022) - 2021
- [j3]Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl:
mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. 22: 184:1-184:7 (2021) - [j2]Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. 6(64): 3453 (2021) - [c3]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-learning for symbolic hyperparameter defaults. GECCO Companion 2021: 151-152 - [c2]Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl:
Learning multiple defaults for machine learning algorithms. GECCO Companion 2021: 241-242 - [d2]Florian Pfisterer, Christoph Kern, Susanne Dandl, Mathew Sun, Michael P. Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. Zenodo, 2021 - [i14]Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl:
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. CoRR abs/2104.00629 (2021) - [i13]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 abs/2104.02705 (2021) - [i12]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR abs/2106.05767 (2021) - [i11]Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl:
Mutation is all you need. CoRR abs/2107.07343 (2021) - [i10]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. CoRR abs/2109.03670 (2021) - [i9]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 abs/2111.14756 (2021) - 2020
- [i8]David Rügamer, Florian Pfisterer, Bernd Bischl:
Neural Mixture Distributional Regression. CoRR abs/2010.06889 (2020) - [i7]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 abs/2011.02407 (2020)
2010 – 2019
- 2019
- [j1]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): 1903 (2019) - [c1]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: 629-647 - [d1]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. Zenodo, 2019 - [i6]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 abs/1902.08999 (2019) - [i5]Florian Pfisterer, Stefan Coors, Janek Thomas, Bernd Bischl:
Multi-Objective Automatic Machine Learning with AutoxgboostMC. CoRR abs/1908.10796 (2019) - [i4]Florian Pfisterer, Janek Thomas, Bernd Bischl:
Towards Human Centered AutoML. CoRR abs/1911.02391 (2019) - [i3]Florian Pfisterer, Laura Beggel, Xudong Sun, Fabian Scheipl, Bernd Bischl:
Benchmarking time series classification - Functional data vs machine learning approaches. CoRR abs/1911.07511 (2019) - 2018
- [i2]Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl:
Learning Multiple Defaults for Machine Learning Algorithms. CoRR abs/1811.09409 (2018) - 2016
- [i1]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 abs/1609.06146 (2016)
Coauthor Index
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last updated on 2024-11-13 23:50 CET by the dblp team
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