Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]. - Joeran Beel; Bryan Tyrell; Edward Bergman; Andrew Collins 002; Shahad Nagoor (CoRR, 2020)

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

PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. - Neeratyoy Mallik; Edward Bergman; Carl Hvarfner; Danny Stoll; Maciej Janowski; Marius Lindauer; Luigi Nardi; Frank Hutter (NeurIPS, 2023)

Don't Waste Your Time: Early Stopping Cross-Validation. - Edward Bergman; Lennart Purucker; Frank Hutter (AutoML, 2024)

Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. - Shuhei Watanabe; Neeratyoy Mallik; Edward Bergman; Frank Hutter (AutoML, 2024)

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. - Herilalaina Rakotoarison; Steven Adriaensen; Neeratyoy Mallik; Samir Garibov; Edward Bergman; Frank Hutter (CoRR, 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)

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)