Marcel Gehrke

- Research Assistant -

Institut für Informationssysteme
Universität zu Lübeck
Ratzeburger Allee 160 ( Gebäude 64 - 2.OG )
D-23562 Lübeck

Telefon: +49 451 3101 5714
Fax : +49 451 3101 5704
email :  E-Mail an Marcel Gehrke

Curriculum Vitae

Since April 2017 - Research Assistant Institut für Informationssysteme at Universität zu Lübeck

Research Interests

Machine Learning
Probabilistic Graphical Models
Lifted Inference
Temporal and Relational Models

For my PhD, I work on probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently and to obtain a compact representation, the lifted junction tree algorithm (LJT) extends LVE. In my thesis, I extend the formalism and respectively LJT to handle temporal aspects. To be more precise, I am interested in solving inference problems, e.g. smoothing, filtering, and prediction, efficiently and to learn relational temporal models from data.

Publications

2018

Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm
to be published in: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, 2018, Springer
Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
to be published in: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, 2018, Springer
Simon Schiff, Marcel Gehrke, Ralf Möller: Efficient Enriching of Synthesized Relational Patient Data with Time Series Data
to be published in: Proceedings of the Workshop on Healthcare Interoperability and Pervasive Intelligent System, 2018, Elsevier Science
Marcel Gehrke, Tanya Braun, Ralf Möller: Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
to be published in: Proceedings of KI 2018: Advances in Artificial Intelligence, 2018, Springer
Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
in: 8th International Workshop on Statistical Relational AI at the 27th International Joint Conference on Artificial Intelligence, 2018
Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Hindsight Queries with Lifted Dynamic Junction Trees
in: 8th International Workshop on Statistical Relational AI at the 27th International Joint Conference on Artificial Intelligence, 2018
Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Towards Lifted Maximum Expected Utility
in: Proceedings of the First Joint Workshop on Artificial Intelligence in Health in Conjunction with the 27th IJCAI, the 23rd ECAI, the 17th AAMAS, and the 35th ICML, 2018, CEUR-WS.org, CEUR Workshop Proceedings, Vol.2142, p.93-96
Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Dynamic Junction Tree Algorithm
in: Proceedings of the International Conference on Conceptual Structures, 2018, Springer, p.55-69

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