Marcel Gehrke, M.Sc.

- 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 : Öffnet ein Fenster zum Versenden einer E-Mail 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

2019

  • Marcel Gehrke, Tanya Braun, Ralf Möller: Relational Forward Backward Algorithm for Multiple Queries
    to be published in: Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS-19), 2019, AAAI Press
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser: Lifted Maximum Expected Utility
    to be published in: Postproceedings of the First Workshop on Artificial Intelligence in Health, 2019, Springer
    BibTeX
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2018

  • Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018, Springer, p.543-555,
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of the AI 2018: Advances in Artificial Intelligence, 2018, Springer, p.556-562
    DOI BibTeX
  • Simon Schiff, Marcel Gehrke, Ralf Möller: Efficient Enriching of Synthesized Relational Patient Data with Time Series Data
    in: Procedia Computer Science, 2018, Vol.141, p.531 - 538, The 8th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2018) / Affiliated Workshops
    DOI BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    in: Proceedings of KI 2018: Advances in Artificial Intelligence, 2018, Springer, p.38-45
    DOI BibTeX
  • 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
    Website BibTeX
  • 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
    Website BibTeX
  • 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
    Website BibTeX
  • 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
    DOI BibTeX
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