Dr. Marcel Wever

Research Focus

My research is centered around automated machine learning (AutoML) and related topics such as meta learning, hyperparameter optimization, algorithm configuration and algorithm selection, as well as supervised learning methods. Specifically, I am interested in methods for multi-label classification. Beyond that, my research interests are widespread and include uncertainty quantification, evolutionary machine learning, machine learning in IT security, part of speech tagging, service-oriented software architectures, and (co-)active learning.

Research Areas

AutoML, Uncertainty

Conference/Workshop Talks

• Green AutoML - Better sustainability for and via AutoML, Franco-German Workshop on Artificial Intelligence, 2022
• A flexible class of dependence-aware multi-label loss functions, ECML/PKDD, 2022
• LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification, Symposium on Intelligent Data Analysis, 2020
• Automating Multi-Label Classification Extending ML-Plan, SYNTH SMiLe Research Camp, 2020
• Towards Automated Machine Learning for Multi-Label Classification, European Conference on Data Analysis, 2019
• Automating Multi-Label Classification Extending ML-Plan, COSEAL Workshop, 2019
• Automating Multi-Label Classification Extending ML-Plan, AutoML Workshop @ ICML, 2019
• Reduction Stumps for Multi-Class Classification, Symposium on Intelligent Data Analysis, 2018
• HASCO: Hierarchical Algorithm Selection and Configuration, COSEAL Workshop, 2018
• ML-Plan: Automated Machine Learning for Multi-Class and Multi-Label Cl, European Conference on Data Analysis, 2018
• ML-Plan: Automated Machine Learning via Hierarchical Planning, ECML/PKDD, 2018
• Ensembles of Evolved Nested Dichotomies for Classification, GECCO, 2018
• Automated Machine Learning: Hierarchical Planning Versus Evolutionary Optimization, CI Workshop, 2017

Teaching Activities

• Lecture: "Automated Algorithm Configuration and Design," SuSe 2023 (lecturer, tutor)
• Seminar: "Recent Advances in Machine Learning," SuSe 2023 (co-organizer, mentor)
• Practical course: "AutoML Workbench," WiSE 2022/2023 (organizer, mentor)
• Seminar: "Evoluationary Algorithms," Winter 2022/2023 (co-organizer, mentor)
• Lecture: "Automated Algorithm Configuration and Design," SuSe 2022 (lecturer, tutor)
• Seminar: "Recent Advances in Machine Learning," SuSe 2022 (co-organizer, mentor)
• Seminar: "Algorithm Selection," SuSe 2022 (co-organizer, mentor)
• Lecture: "Foundations of Machine Learning," WiSe 2021/2022 (tutor)
• Seminar: "Recent Advances in Machine Learning," WiSe 2021/2022 (co-organizer, mentor)
• Project group: "On-The-Fly Machine Learning Part 2," WiSe 2018/2019 (co-organizer, mentor) 
• Seminar: "Advanced Topics in Automated Machine Learning," WiSe 2018/2019 (co-organizer, mentor) 
• Project group: "On-The-Fly Machine Learning Part 1," SuSe 2018 (co-organizer, mentor)
• Seminar: "Automated Machine Learning," SuSe 2018 (co-organizer, mentor)
• Lecture: "Introduction to Programming 2," SuSe 2014 (tutor)
• Lecture: "Introduction to Programming Languages," SuSe 2014 (tutor)
• Lecture: "Introduction to Programming 1," WiSe 2013/2014 (tutor)
• Lecture: "Introduction to Programming 2," SuSe 2013 (tutor)