Lifted Dynamic Junction Tree Algorithm

We 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. We extend the formalism and respectively LJT to handle temporal aspects. To be more precise, we combine the advantages of LJT and the interface algorithm in LDJT, which efficiently solves the inference problems filtering and prediction.

Additionally, we are interested in solving other inference problems, e.g. smoothing, and to learn relational temporal models from data.

LDJT is supported by CISCO. The work is carried out as part of Jointlab 1 within the COPICOH center for connected health.

Publications

2018

  • Marcel Gehrke, Tanya Braun, Ralf Möller: Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm
    wird veröffentlicht in: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, 2018, Springer
    BibTeX
  • Marcel Gehrke, Tanya Braun, Ralf Möller: Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
    wird veröffentlicht in: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, 2018, Springer
    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
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  • 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
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  • 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
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