Marcel Wever

CoRR, 2024

Information Leakage Detection through Approximate Bayes-optimal Prediction.

[BibT_eX]

[DOI]

Pritha Gupta

CoRR, 2024

2023

PyExperimenter: Easily distribute experiments and track results.

[BibT_eX]

[DOI]

J. Open Source Softw., June, 2023

Algorithm selection on a meta level.

[BibT_eX]

[DOI]

Mach. Learn., April, 2023

Naive automated machine learning.

[BibT_eX]

[DOI]

Mach. Learn., April, 2023

Towards Green Automated Machine Learning: Status Quo and Future Directions.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2023

Iterative Deepening Hyperband.

[BibT_eX]

[DOI]

CoRR, 2023

A Survey of Methods for Automated Algorithm Configuration (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets.

[BibT_eX]

[DOI]

David Schubert

Pritha Gupta

Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification.

[BibT_eX]

[DOI]

Miran Özdogan

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

2022

Automated machine learning for multi-label classification.

[BibT_eX]

[DOI]

PhD thesis, 2022

A flexible class of dependence-aware multi-label loss functions.

[BibT_eX]

[DOI]

Mach. Learn., 2022

A Survey of Methods for Automated Algorithm Configuration.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2022

Hyperparameter optimization in deep multi-target prediction.

[BibT_eX]

[DOI]

CoRR, 2022

2021

AutoML for Multi-Label Classification: Overview and Empirical Evaluation.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning.

[BibT_eX]

[DOI]

CoRR, 2021

Annotation Uncertainty in the Context of Grammatical Change.

[BibT_eX]

[DOI]

CoRR, 2021

Naive Automated Machine Learning - A Late Baseline for AutoML.

[BibT_eX]

[DOI]

CoRR, 2021

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance.

[BibT_eX]

[DOI]

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

2020

Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.

[BibT_eX]

[DOI]

Lorijn van Rooijen

Heiko Hamann

Evol. Comput., 2020

Towards Meta-Algorithm Selection.

[BibT_eX]

[DOI]

Alexander Tornede

CoRR, 2020

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.

[BibT_eX]

[DOI]

Stefan Heid

CoRR, 2020

AutoML for Predictive Maintenance: One Tool to RUL Them All.

[BibT_eX]

[DOI]

Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

Hybrid Ranking and Regression for Algorithm Selection.

[BibT_eX]

[DOI]

Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

Extreme Algorithm Selection with Dyadic Feature Representation.

[BibT_eX]

[DOI]

Alexander Tornede

Proceedings of the Discovery Science - 23rd International Conference, 2020

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.

[BibT_eX]

[DOI]

Proceedings of The 12th Asian Conference on Machine Learning, 2020

2019

From Automated to On-The-Fly Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 49. Jahrestagung der Gesellschaft für Informatik, 50 Jahre Gesellschaft für Informatik, 2019

2018

ML-Plan: Automated machine learning via hierarchical planning.

[BibT_eX]

[DOI]

Mach. Learn., 2018

Automated Multi-Label Classification based on ML-Plan.

[BibT_eX]

[DOI]

CoRR, 2018

Automated Machine Learning Service Composition.

[BibT_eX]

[DOI]

CoRR, 2018

Reduction Stumps for Multi-class Classification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Ensembles of evolved nested dichotomies for classification.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

(WIP) Towards the Automated Composition of Machine Learning Services.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Services Computing, 2018

On-the-Fly Service Construction with Prototypes.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Services Computing, 2018

2017

Active coevolutionary learning of requirements specifications from examples.

[BibT_eX]

[DOI]