Mutation is all you need. - Lennart Schneider; Florian Pfisterer; Martin Binder; Bernd Bischl (CoRR, 2021)

YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (CoRR, 2021)

mlr3pipelines - Flexible Machine Learning Pipelines in R. - Martin Binder; Florian Pfisterer; Michel Lang; Lennart Schneider; Lars Kotthoff; Bernd Bischl (J. Mach. Learn. Res., 2021)

YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. - Florian Pfisterer; Lennart Schneider; Julia Moosbauer; Martin Binder; Bernd Bischl (AutoML, 2022)

Tackling Neural Architecture Search With Quality Diversity Optimization. - Lennart Schneider; Florian Pfisterer; Paul Kent; Jürgen Branke; Bernd Bischl; Janek Thomas (AutoML, 2022)

A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. - Lennart Schneider; Florian Pfisterer; Janek Thomas; Bernd Bischl (GECCO Companion, 2022)

HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. - Lennart Schneider; Lennart Schäpermeier; Raphael Patrick Prager; Bernd Bischl; Heike Trautmann; Pascal Kerschke (PPSN (1), 2022)

Multi-Objective Hyperparameter Optimization - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (CoRR, 2022)

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. - Lennart Schneider; Lennart Schäpermeier; Raphael Patrick Prager; Bernd Bischl; Heike Trautmann; Pascal Kerschke (CoRR, 2022)

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. - Julia Moosbauer; Martin Binder; Lennart Schneider; Florian Pfisterer; Marc Becker; Michel Lang; Lars Kotthoff; Bernd Bischl (IEEE Trans. Evol. Comput., 2022)

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. - Lennart Oswald Purucker; Lennart Schneider; Marie Anastacio; Joeran Beel; Bernd Bischl; Holger H. Hoos (AutoML, 2023)

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. - Raphael Patrick Prager; Konstantin Dietrich; Lennart Schneider; Lennart Schäpermeier; Bernd Bischl; Pascal Kerschke; Heike Trautmann; Olaf Mersmann (FOGA, 2023)

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. - Lennart Schneider; Bernd Bischl; Janek Thomas (GECCO, 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Multi-Objective Hyperparameter Optimization in Machine Learning - An Overview. - Florian Karl; Tobias Pielok; Julia Moosbauer; Florian Pfisterer; Stefan Coors; Martin Binder; Lennart Schneider; Janek Thomas; Jakob Richter; Michel Lang; Eduardo C. Garrido-Merchán; Jürgen Branke; et al. (ACM Trans. Evol. Learn. Optim., 2023)

An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice. - Markus Bauer; Lennart Schneider; Marit Bernhardt; Christoph Augenstein; Glen Kristiansen; Bogdan Franczyk (ICEIS (1), 2024)

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. - Thomas Nagler; Lennart Schneider; Bernd Bischl; Matthias Feurer (NeurIPS, 2024)

Hyperband-based Bayesian Optimization for Black-box Prompt Selection. - Lennart Schneider; Martin Wistuba; Aaron Klein; Jacek Golebiowski; Giovanni Zappella; Felice Antonio Merra (CoRR, 2024)

Overtuning in Hyperparameter Optimization. - Lennart Schneider; Bernd Bischl; Matthias Feurer (CoRR, 2025)