Surrogate Benchmarks for Hyperparameter Optimization. - Katharina Eggensperger; Frank Hutter; Holger H. Hoos; Kevin Leyton-Brown (MetaSel@ECAI, 2014)

Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. - Katharina Eggensperger; Frank Hutter; Holger H. Hoos; Kevin Leyton-Brown (AAAI, 2015)

Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (NIPS, 2015)

Automatic bone parameter estimation for skeleton tracking in optical motion capture. - Tobias Schubert 002; Katharina Eggensperger; Alexis Gkogkidis; Frank Hutter; Tonio Ball; Wolfram Burgard (ICRA, 2016)

Efficient Parameter Importance Analysis via Ablation with Surrogates. - Andre Biedenkapp; Marius Lindauer; Katharina Eggensperger; Frank Hutter; Chris Fawcett; Holger H. Hoos (AAAI, 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)

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)

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)

Predicting Runtime Distributions using Deep Neural Networks. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (CoRR, 2017)

Neural Networks for Predicting Algorithm Runtime Distributions. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (IJCAI, 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)

Auto-sklearn: Efficient and Robust Automated Machine Learning. - Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Tobias Springenberg; Manuel Blum 002; Frank Hutter (Automated Machine Learning, 2019)

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. - Marius Lindauer; Matthias Feurer; Katharina Eggensperger; André Biedenkapp; Frank Hutter (CoRR, 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 008; Frank Hutter (CoRR, 2019)

Pitfalls and Best Practices in Algorithm Configuration. - Katharina Eggensperger; Marius Lindauer; Frank Hutter (J. Artif. Intell. Res., 2019)

Auto-Sklearn 2.0: The Next Generation. - Matthias Feurer; Katharina Eggensperger; Stefan Falkner; Marius Lindauer; Frank Hutter (CoRR, 2020)

Neural Model-based Optimization with Right-Censored Observations. - Katharina Eggensperger; Kai Haase; Philipp Müller 008; Marius Lindauer; Frank Hutter (CoRR, 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 025; Carola Doerr; Marius Lindauer; Frank Hutter (CoRR, 2020)

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. - Katharina Eggensperger; Philipp Müller 008; Neeratyoy Mallik; Matthias Feurer; René Sass; Aaron Klein; Noor H. Awad; Marius Lindauer; Frank Hutter (NeurIPS Datasets and Benchmarks, 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)

Meta-Learning a Real-Time Tabular AutoML Method For Small Data. - Noah Hollmann; Samuel Müller 005; Katharina Eggensperger; Frank Hutter (CoRR, 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)

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)

Advanced hyperparameter optimization: performance modelling and efficient benchmarking. - Katharina Eggensperger (2022)

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. - Noah Hollmann; Samuel Müller 005; Katharina Eggensperger; Frank Hutter (ICLR, 2023)

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. - Matthias Feurer; Katharina Eggensperger; Edward Bergman; Florian Pfisterer; Bernd Bischl; Frank Hutter (IDA, 2023)

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 (ICML, 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)

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)

Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning. - Amir Rezaei Balef; Claire Vernade; Katharina Eggensperger (CoRR, 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; et al. (CoRR, 2025)

In-Context Decision Making for Optimizing Complex AutoML Pipelines. - Amir Rezaei Balef; Katharina Eggensperger (CoRR, 2025)