Katharina Eggensperger

According to our database, Katharina Eggensperger authored at least 27 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Advanced hyperparameter optimization: performance modelling and efficient benchmarking.

Katharina Eggensperger

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

Meta-Learning a Real-Time Tabular AutoML Method For Small Data.

Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

CoRR, 2022

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

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

Neural Model-based Optimization with Right-Censored Observations.

Katharina Eggensperger, Kai Haase, Philipp Müller, 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

Pitfalls and Best Practices in Algorithm Configuration.

Katharina Eggensperger, Marius Lindauer, Frank Hutter

J. Artif. Intell. Res., 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

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

2018

Efficient benchmarking of algorithm configurators via model-based surrogates.

Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

Mach. Learn., 2018

Neural Networks for Predicting Algorithm Runtime Distributions.

Katharina Eggensperger, Marius Lindauer, Frank Hutter

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

Predicting Runtime Distributions using Deep Neural Networks.

Katharina Eggensperger, Marius Lindauer, Frank Hutter

CoRR, 2017

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology.

Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball

CoRR, 2017

Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG.

Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

CoRR, 2017

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates.

Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

CoRR, 2017

Efficient Parameter Importance Analysis via Ablation with Surrogates.

Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Automatic bone parameter estimation for skeleton tracking in optical motion capture.

Tobias Schubert, Katharina Eggensperger, Alexis Gkogkidis, Frank Hutter, Tonio Ball, Wolfram Burgard

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 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

Efficient Benchmarking of Hyperparameter Optimizers via Surrogates.

Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

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

2014

Surrogate Benchmarks for Hyperparameter Optimization.

Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

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