Marcel Wever

发表

Active coevolutionary learning of requirements specifications from examples

Heiko Hamann, Lorijn van Rooijen, Marcel Wever, 2017, GECCO.

ML-Plan for Unlimited-Length Machine Learning Pipelines

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, ICML 2018.

Towards Meta-Algorithm Selection pdf

Alexander Tornede, Marcel Wever, Eyke Hullermeier, 2020, ArXiv.

Extreme Algorithm Selection With Dyadic Feature Representation pdf

Eyke Hüllermeier, Alexander Tornede, Marcel Wever, 2020, DS.

Hybrid Ranking and Regression for Algorithm Selection

Eyke Hüllermeier, Alexander Tornede, Marcel Wever, 2020, KI.

Automated Machine Learning Service Composition pdf

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, ArXiv.

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

Eyke Hüllermeier, Stefan Werner, Alexander Tornede, 2020, ACML.

Active Learning of User Requirement Specifications in Dynamic Software Service Markets

Marcel Wever, 2017 .

Ensembles of evolved nested dichotomies for classification

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, GECCO.

On-the-Fly Service Construction with Prototypes

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, 2018 IEEE International Conference on Services Computing (SCC).

ML-Plan: Automated machine learning via hierarchical planning

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, Machine Learning.

Towards Automated Machine Learning for Multi-Label Classification

Felix Mohr, Marcel Wever, Eyke Hüllermeier, 2019 .

(WIP) Towards the Automated Composition of Machine Learning Services

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, 2018 IEEE International Conference on Services Computing (SCC).

Automated Multi-Label Classification based on ML-Plan pdf

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, ArXiv.

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

Heiko Hamann, Lorijn van Rooijen, Marcel Wever, 2019, Evolutionary Computation.

Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2017 .

Reduction Stumps for Multi-class Classification

Eyke Hüllermeier, Felix Mohr, Marcel Wever, 2018, IDA.

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

Marcel Wever, Michael Rapp, Eyke Hullermeier, 2020, Mach. Learn..

Automating Multi-Label Classification Extending ML-Plan

Eyke Hüllermeier, Alexander Tornede, Felix Mohr, 2019 .

From Automated to On-The-Fly Machine Learning

Eyke Hüllermeier, Alexander Tornede, Felix Mohr, 2019, GI-Jahrestagung.

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

Eyke Hüllermeier, Alexander Tornede, Felix Mohr, 2020, IDA.

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

Marcel Wever, Eyke Hüllermeier, Stefan Heid, 2020, ArXiv.

Supporting the Cognitive Process in Annotation Tasks

Eyke Hüllermeier, Nina Seemann, Michaela Geierhos, 2018 .

Naive Automated Machine Learning

Felix Mohr, Marcel Wever, 2021 .

Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance

Eyke Hüllermeier, Tanja Tornede, Alexander Tornede, 2021, GECCO.

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

Felix Mohr, Alexander Tornede, Marcel Wever, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

AutoML for Multi-Label Classification: Overview and Empirical Evaluation

Felix Mohr, Alexander Tornede, Marcel Wever, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning pdf

Felix Mohr, Alexander Tornede, Marcel Wever, 2021, ArXiv.

Naive Automated Machine Learning - A Late Baseline for AutoML pdf

Felix Mohr, Marcel Wever, 2021, ArXiv.

Annotation Uncertainty in the Context of Grammatical Change pdf

Michaela Geierhos, Marcel Wever, Eyke Hullermeier, 2021, International Journal of Corpus Linguistics.

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

Eyke Hüllermeier, Alexander Tornede, Marcel Wever, 2021, PAKDD.

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

Felix Mohr, Tanja Tornede, Alexander Tornede, 2021, ArXiv.

Algorithm selection on a meta level pdf

Tanja Tornede, Alexander Tornede, Marcel Wever, 2021, Machine Learning.