Matthias Feurer

According to our database, Matthias Feurer authored at least 34 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2025

Overtuning in Hyperparameter Optimization.

Lennart Schneider, Bernd Bischl, Matthias Feurer

CoRR, June, 2025

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks.

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

CoRR, June, 2025

openml/OpenML-Paper-Impact-Analysis: v1.0.4 - Code Release for paper "OpenML: Insights from ten years and more than a thousand papers".

Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Mueller, László Németh, Luis Oala, Lennart Oswald Purucker, Ravi Sahithaya, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever

Dataset, May, 2025

CAPO: Cost-Aware Prompt Optimization.

Tom Zehle, Moritz Schlager, Timo Heiß, Matthias Feurer

CoRR, April, 2025

OpenML: Insights from 10 years and more than a thousand papers.

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

Patterns, 2025

2024

AMLTK: A Modular AutoML Toolkit in Python.

Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger

J. Open Source Softw., 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

Interpretable Machine Learning for TabPFN.

David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer

Proceedings of the Explainable Artificial Intelligence, 2024

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization.

Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Position: A Call to Action for a Human-Centered AutoML Paradigm.

Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Why We Must Rethink Empirical Research in Machine Learning.

Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

PFNs Are Flexible Models for Real-World Bayesian Optimization.

Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter

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

PFNs4BO: In-Context Learning for Bayesian Optimization.

Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter

Proceedings of the International Conference on Machine Learning, 2023

2022

Robust and efficient automated machine learning: systems, infrastructure and advances in hyperparameter optimization.

Matthias Feurer

PhD thesis, 2022

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter

J. Mach. Learn. Res., 2022

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter

J. Mach. Learn. Res., 2022

2021

OpenML-Python: an extensible Python API for OpenML.

Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter

J. Mach. Learn. Res., 2021

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter

CoRR, 2021

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO.

Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

OpenML Benchmarking Suites.

Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Squirrel: A Switching Hyperparameter Optimizer.

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

CoRR, 2020

Auto-Sklearn 2.0: The Next Generation.

Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter

CoRR, 2020

2019

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters.

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter

CoRR, 2019

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters.

Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter

CoRR, 2019

Towards Automatically-Tuned Deep Neural Networks.

Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

Auto-sklearn: Efficient and Robust Automated Machine Learning.

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

Hyperparameter Optimization.

Matthias Feurer, Frank Hutter

Proceedings of the Automated Machine Learning - Methods, Systems, Challenges, 2019

2018

Scalable Meta-Learning for Bayesian Optimization.

Matthias Feurer, Benjamin Letham, Eytan Bakshy

CoRR, 2018

2017

OpenML Benchmarking Suites and the OpenML100.

Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren

CoRR, 2017

2016

Towards Automatically-Tuned Neural Networks.

Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

Proceedings of the 2016 Workshop on Automatic Machine Learning, 2016

2015

Efficient and Robust Automated Machine Learning.

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Initializing Bayesian Hyperparameter Optimization via Meta-Learning.

Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters.

Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

Proceedings of the International Workshop on Meta-learning and Algorithm Selection co-located with 21st European Conference on Artificial Intelligence, 2014