Publications

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, 2024

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

[arXiv] [DOI] [URL]

2023

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

[arXiv] [Code] [DOI]

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

[arXiv] [Code] [URL]

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

[arXiv] [Code] [URL] [Video]

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

[arXiv] [Code] [URL]

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

[arXiv] [Code] [URL]

Advanced Hyperparameter Optimization: Performance Modelling and Efficient Benchmarking

Katharina Eggensperger
In: University of Freiburg, Department of Computer Science, 2022

[URL]

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 Awad, Marius Lindauer, Frank Hutter
In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS'21), 2021

[arXiv] [Code] [Slides] [Poster] [URL] [Video]

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

[arXiv] [Code]

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

[arXiv]

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

[arXiv] [Code]

Pitfalls and Best Practices in Algoritm Configuration

Katharina Eggensperger, Marius Lindauer, Frank Hutter
In: Journal of Artificial Intelligence (JAIR), 2019

[arXiv] [DOI]

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

[arXiv] [Slides]

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

[arXiv] [Code] [DOI] [URL]

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

[arXiv] [URL]

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

[arXiv] [Slides] [Poster] [DOI] [URL]

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

[PDF]

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

[arXiv] [DOI] [URL]

Filtering Outlier in Bayesian Optimization

Ruben Martinez-Cantin, Kevin Tee, Mike McCourt, Katharina Eggensperger
In: NeurIPS Workshop on Bayesian Optimization (BayesOpt'17), 2017

[Poster] [PDF] [Supp]

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

[Code] [Poster] [DOI] [URL] [PDF]

2016

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

[PDF]

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

[URL] [PDF]

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

[Code] [Poster] [URL]

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

[Slides] [Poster] [PDF]

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

[arXiv] [Poster] [DOI] [PDF]

2014

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

[Slides] [PDF]

2013

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

[Code] [Poster] [PDF]