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publications
Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
*Katharina Eggensperger*, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger H. Hoos, Kevin Leyton-Brown
In NeurIPS workshon on Bayesian Optimization in Theory and Practice (BayesOpt'13), 2013
Surrogate Benchmarks for Hyperparameter Optimization
*Katharina Eggensperger*, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
In ECAI workshop on Metalearning and Algoritmm Selection (MetaSel'14), 2014
Note: Superseded by “Efficient Benchmarking of Hyperparameter Optimizers via Surrogates” in AAAI 2015
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates
*Katharina Eggensperger*, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15), 2015
Note: Superseding “Surrogate Benchmarks for Hyperparameter Optimization” in MetaSel @ ECAI 2014
Methods for Improving Bayesian Optimization for AutoML
Matthias Feurer, Aaron Klein, *Katharina Eggensperger*, J. Tobias Springenberg, Manuel Blum, Frank Hutter
In ICML Workshop on Automated Machine Learning (AutoML'15), 2015
Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, *Katharina Eggensperger*, J. Tobias Springenberg, Manuel Blum, Frank Hutter
In Advances in Neural Information Processing Systems 28 (NeurIPS'15), 2015
Automatic Bone Estimation for Skeleton Tracking in Optical Motion Capture
Tobias Schubert, *Katharina Eggensperger*, Alexis Gkogkidis, Frank Hutter, Tonio Ball, Wolfram Burgard
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'16), 2015
video showing the results of the optimization procedure
Hyperparameter Optimization for Machine Learning Problems in BCI (Abstract)
Andreas Meinel, *Katharina Eggensperger*, Michael Tangermann, Frank Hutter
In Proceedings of the International Brain Computer Interface Meeting, 2016
Efficient Parameter Importance Analysis via Ablation with Surrogates
André Biedenkapp, Marius Lindauer, *Katharina Eggensperger*, Chris Fawcett, Holger H. Hoos, Frank Hutter
In Proceedings of the Thirty-First Conference on Artificial Intelligence (AAAI'17), 2017
Filtering Outlier in Bayesian Optimization
Ruben Martinez-Cantin, Kevin Tee, Mike McCourt, *Katharina Eggensperger*
In NeurIPS Workshop on Bayesian Optimization (BayesOpt'17), 2017
Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization
Robin Schirrmeister, J. Tobias Springenberg, Lukas Fiederer, Martin Glasstetter, *Katharina Eggensperger*, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball
In Human Brain Mapping, 2017
Practical Automated Machine Learning for the AutoML Challenger 2018
Matthias Feurer, *Katharina Eggensperger*, Stefan Falkner, Marius Lindauer, Frank Hutter
In ICML Workshop on Automated Machine Learning (AutoML'18), 2018
Neural Networks for Predicting Algorithm Runtime Distribution
*Katharina Eggensperger*, Marius Lindauer, Frank Hutter
In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'18), 2018
Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates
*Katharina Eggensperger*, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
In Machine Learning (MLJ), 2018
Auto-Sklearn: Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, *Katharina Eggensperger*, J. Tobias Springenberg, Manuel Blum, Frank Hutter
In AutoML: Methods, Systems, Challenges, 2019
Adapted version of the 2015 NeurIPS paper “Efficient and Robust Automated Machine Learning
Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameter
Marius Lindauer, Matthias Feurer, *Katharina Eggensperger*, André Biedenkapp, Frank Hutter
In IJCAI Workshop on Data Science meets Optimisatio (DSO'10), 2019
Pitfalls and Best Practices in Algoritm Configuration
*Katharina Eggensperger*, Marius Lindauer, Frank Hutter
In Journal of Artificial Intelligence (JAIR), 2019
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization and Analysis of Hyperparameters
Marius Lindauer, *Katharina Eggensperger*, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
In arXiv:1908.06756 [cs.LG], 2019
Neural Model-based Optimization with Right-Censored Observations
*Katharina Eggensperger*, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
In arXiv:2009.13828 [cs.AI], 2020
Squirrel: A Switching Hyperparameter Optimizer Description of the Entry by AutoML.org & IOHProfiler to the NeurIPS 2020 BBO Challenge
Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, *Katharina Eggensperger*, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter
In arXiv:2012.08180 [cs.LG], 2020
Optimizer description for the NeurIPS 2020 BBO competition. Squirrel won the competition´s warm-starting friendly leaderboard
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
*Katharina Eggensperger*, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter
In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS'21), 2021
![[Poster]]( http://ml.informatik.uni-freiburg.de/wp-content/uploads/2021/12/2020_HPOBench.png ){kind=link}
Advanced Hyperparameter Optimization: Performance Modelling and Efficient Benchmarking
*Katharina Eggensperger*
In University of Freiburg, Department of Computer Science, 2022
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer, *Katharina Eggensperger*, Stefan Falkner, Marius Lindauer, Frank Hutter
In Journal of Machine Learning Research (JMLR), 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
In Journal of Machine Learning (JMLR) -- MLOSS, 2022
TabPFN: A Transformer that Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, *Katharina Eggensperger*, Frank Hutter
In NeurIPS Workshop on Table Representation Learning (TRL'22), 2022
Received the best paper award
TabPFN: A Transformer that Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, *Katharina Eggensperger*, Frank Hutter
In International Conference of Learning Representations (ICLR'23), 2023
Note: Superseding “TabPFN: A Transformer that Solves Small Tabular Classification Problems in a Second” in TLR @ NeurIPS 2022
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Matthias Feurer, *Katharina Eggensperger*, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
In Proceedings of the Symposium on Intelligen Data Analysis (IDA'23), 2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer, *Katharina Eggensperger*, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
In Journal of Artificial Intelligence (JAIR), 2024
Towards Quantifying the Effect of Dataset Selection for Benchmarking Tabular Machine Learning Approaches
Ravin Kohli, Matthias Feurer, Bernd Bischl, *Katharina Eggensperger*, Frank Hutter
In Data-centric Machine Learning Research (DMLR) workshop at ICLR'24, 2024
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, Julian Lange, *Katharina Eggensperger*, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
In International Conference on Machine Learning (ICML'24), 2024
Towards Bandit-based Optimization for Automated Machine Learning
*Amir Rezaei Balef*, Claire Vernade, *Katharina Eggensperger*
In Workshop on practical ML for limited/low resource settings (PML4LRS) at ICLR'24, 2024
How can we quantify, explain, and apply the uncertainty of complex soil maps predicted with neural networks?
Kerstin Rau, *Katharina Eggensperger*, Frank Schneider, Philipp Hennig, Thomas Scholten
In Science of Total Environment (STOTEN), 2024
Towards Localization via Data Embedding for TabPFN
*Mykhailo Koshil*, Thomas Nagler, Matthias Feurer, *Katharina Eggensperger*
In Third Table Representation Learning Workshop (TRL) at NeurIPS'24, 2024
Quantifying spatial uncertainty to improve soil predictions in data-sparse regions
Kerstin Rau, *Katharina Eggensperger*, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten
In EGUsphere, 2025
In-Context Learning of Soft Nearest Neighbor Classifiers for Intelligible Tabular Machine Learning
*Mykhailo Koshil*, Matthias Feurer, *Katharina Eggensperger*
In Forth Table Representation Learning Workshop (TRL) at ACL'24, 2025
In-Context Decision Making for Optimizing Complex AutoML Pipelines
*Amir Balef*, *Katharina Eggensperger*
In European Conference on Artificial Intelligence, 2025
talks
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teaching
Seminar: Automated Machine Learning and Hyperparameter Optimization
Seminar, University of Tübingen, 2023
Have you ever trained an ML model and wondered how to efficiently optimize its hyperparameters? Then this seminar is for you!
Seminar: AutoML in the Age of Large Pre-trained Models
Seminar, University of Tübingen, 2024
Have you heard about AutoML? Do you wonder what LLMs can do for AutoML and vice versa? Then this seminar is for you!
Seminar: (Auto-)ML for tabular data
Seminar, University of Tübingen, 2024
What is tabular data? And which model would you use for it? Why is tabular data challenging for machine learning? And how would you compare learning approaches on tabular data?
Seminar: Tabular Machine Learning
Seminar, TU Dortmund, 2025
Tabular data is everywhere and often at the core of data science tasks, from healthcare to e-commerce and the natural sciences. Yet it comes with unique challenges and research questions for machine learning: