Matthias Feurer

47 papers A* 5B 1Journal 28Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters. CoRR Fiona Katharina Ewald, Martin Binder, Matthias Feurer, Bernd Bischl, Giuseppe Casalicchio
2026	—	`conf`	promptolution: A Unified, Modular Framework for Prompt Optimization. EACL (System Demonstrations) Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer
2025	J	`jnl`	Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network. CoRR Theresa Eimer, Lennart Schäpermeier, André Biedenkapp, Alexander Tornede, Lars Kotthoff, Pieter Leyman, Matthias Feurer, Katharina Eggensperger, Kaitlin Maile, Tanja Tornede, Anna Kozak, Ke Xue, Marcel Wever, Mitra Baratchi, Damir Pulatov, Heike Trautmann, Haniye Kashgarani, Marius Lindauer
2025	—	`conf`	CAPO: Cost-Aware Prompt Optimization. AutoML Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer
2025	J	`jnl`	CAPO: Cost-Aware Prompt Optimization. CoRR Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer
2025	J	`jnl`	OpenML: Insights from 10 years and more than a thousand papers. Patterns Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Müller, László Németh, Luis Oala, Lennart Purucker, Sahithya Ravi, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever
2025	—	`conf`	Overtuning in Hyperparameter Optimization. AutoML Lennart Schneider, Bernd Bischl, Matthias Feurer
2025	J	`jnl`	Overtuning in Hyperparameter Optimization. CoRR Lennart Schneider, Bernd Bischl, Matthias Feurer
2025	J	`jnl`	carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. CoRR Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, François Clément, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer
2025	J	`jnl`	promptolution: A Unified, Modular Framework for Prompt Optimization. CoRR Tom Zehle, Timo Heiß, Moritz Schlager, Matthias Aßenmacher, Matthias Feurer
2024	J	`jnl`	AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger
2024	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2024	—	`conf`	Interpretable Machine Learning for TabPFN. xAI (2) David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer
2024	J	`jnl`	Interpretable Machine Learning for TabPFN. CoRR David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer
2024	A*	`conf`	Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	J	`jnl`	Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	A*	`conf`	Position: Why We Must Rethink Empirical Research in Machine Learning. ICML Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024	J	`jnl`	Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024	A*	`conf`	Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. NeurIPS Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer
2024	J	`jnl`	Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. CoRR Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer
2023	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2023	B	`conf`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2023	J	`jnl`	PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2023	A*	`conf`	PFNs4BO: In-Context Learning for Bayesian Optimization. ICML Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2022	J	`jnl`	Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. J. Mach. Learn. Res. Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2022	—	`—`	Robust and efficient automated machine learning: systems, infrastructure and advances in hyperparameter optimization. Matthias Feurer
2022	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
2021	—	`conf`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	J	`jnl`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	—	`conf`	OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2021	J	`jnl`	OpenML-Python: an extensible Python API for OpenML. J. Mach. Learn. Res. Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2021	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter
2020	J	`jnl`	Auto-Sklearn 2.0: The Next Generation. CoRR Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Squirrel: A Switching Hyperparameter Optimizer. CoRR Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter
2019	—	`ch.`	Auto-sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
2019	J	`jnl`	BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
2019	—	`ch.`	Hyperparameter Optimization. Automated Machine Learning Matthias Feurer, Frank Hutter
2019	J	`jnl`	OpenML-Python: an extensible Python API for OpenML. CoRR Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2019	J	`jnl`	Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter
2019	—	`ch.`	Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter
2018	J	`jnl`	Scalable Meta-Learning for Bayesian Optimization. CoRR Matthias Feurer, Benjamin Letham, Eytan Bakshy
2017	J	`jnl`	OpenML Benchmarking Suites and the OpenML100. CoRR Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2016	—	`conf`	Towards Automatically-Tuned Neural Networks. AutoML@ICML Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
2015	—	`conf`	Efficient and Robust Automated Machine Learning. NIPS Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
2015	A*	`conf`	Initializing Bayesian Hyperparameter Optimization via Meta-Learning. AAAI Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
2014	—	`conf`	Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters. MetaSel@ECAI Matthias Feurer, Jost Tobias Springenberg, Frank Hutter