From Automated to On-The-Fly Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (GI-Jahrestagung, 2019)

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. - Alexander Tornede; Marcel Wever; Stefan Werner; Felix Mohr; Eyke Hüllermeier (ACML, 2020)

Extreme Algorithm Selection with Dyadic Feature Representation. - Alexander Tornede; Marcel Wever; Eyke Hüllermeier (DS, 2020)

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IDA, 2020)

Hybrid Ranking and Regression for Algorithm Selection. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (KI, 2020)

AutoML for Predictive Maintenance: One Tool to RUL Them All. - Tanja Tornede; Alexander Tornede; Marcel Wever; Felix Mohr; Eyke Hüllermeier (IoT Streams/ITEM@PKDD/ECML, 2020)

Towards Meta-Algorithm Selection. - Alexander Tornede; Marcel Wever; Eyke Hüllermeier (CoRR, 2020)

Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. - Tanja Tornede; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (GECCO, 2021)

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (PAKDD (1), 2021)

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. - Eyke Hüllermeier; Felix Mohr; Alexander Tornede; Marcel Wever (CoRR, 2021)

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

AutoML for Multi-Label Classification: Overview and Empirical Evaluation. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Machine Learning for Online Algorithm Selection under Censored Feedback. - Alexander Tornede; Viktor Bengs; Eyke Hüllermeier (AAAI, 2022)

HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. - Lukas Fehring; Jonas Hanselle; Alexander Tornede (CoRR, 2022)

A Survey of Methods for Automated Algorithm Configuration. - Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (J. Artif. Intell. Res., 2022)

Symbolic Explanations for Hyperparameter Optimization. - Sarah Segel; Helena Graf; Alexander Tornede; Bernd Bischl; Marius Lindauer (AutoML, 2023)

A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). - Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (IJCAI, 2023)

Towards Green Automated Machine Learning: Status Quo and Future Directions. - Tanja Tornede; Alexander Tornede; Jonas Hanselle; Felix Mohr; Marcel Wever; Eyke Hüllermeier (J. Artif. Intell. Res., 2023)

MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. - Tim Ruhkopf; Aditya Mohan; Difan Deng; Alexander Tornede; Frank Hutter; Marius Lindauer (Trans. Mach. Learn. Res., 2023)

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data and Simplifying Meta Level Decisions. - Alexander Tornede (2023)

Algorithm selection on a meta level. - Alexander Tornede; Lukas Gehring; Tanja Tornede; Marcel Wever; Eyke Hüllermeier (Mach. Learn., 2023)

PyExperimenter: Easily distribute experiments and track results. - Tanja Tornede; Alexander Tornede; Lukas Fehring; Lukas Gehring; Helena Graf; Jonas Hanselle; Felix Mohr; Marcel Wever (J. Open Source Softw., 2023)

Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. - Joseph Giovanelli; Alexander Tornede; Tanja Tornede; Marius Lindauer (AAAI, 2024)

Position: A Call to Action for a Human-Centered AutoML Paradigm. - Marius Lindauer; Florian Karl; Anne Klier; Julia Moosbauer; Alexander Tornede; Andreas Müller 024; Frank Hutter; Matthias Feurer; Bernd Bischl (ICML, 2024)

AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. - Alexander Tornede; Difan Deng; Theresa Eimer; Joseph Giovanelli; Aditya Mohan; Tim Ruhkopf; Sarah Segel; Daphne Theodorakopoulos; Tanja Tornede; Henning Wachsmuth; Marius Lindauer (Trans. Mach. Learn. Res., 2024)