Mattis Hartwig, M.Sc.

- externer Doktorand -

 

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

Telefon: +49 451 3101 5713
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Ab 1. Januar 2019 fertigt Herr Hartwig unter der Betreuung von Herrn Prof. Ralf Möller seine Dissertation zum Thema "Baysian Non-Parametrics in Machine Learning" an.

Publikationen

2021

  • Mattis Hartwig, Achim Peters: Cooperation and Social Rules Emerging From the Principle of Surprise Minimization
    in: Frontiers in Psychology, 2021, Vol.11, p.3668
    DOI Website BibTeX
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2020

  • Mattis Hartwig, Ralf Möller: How to Encode Dynamic Gaussian Bayesian Networks as Gaussian Processes?
    in: AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, , Springer, Canberra, ACT, Australia, November 29-30, 2020, Lecture Notes in Computer Science, Vol.12576, p.371-382
    DOI Website BibTeX
  • Mattis Hartwig, Ralf Möller: Lifted Query Answering in Gaussian Bayesian Networks
    in: Proceedings of Machine Learning Research, 2020
    Website BibTeX
  • Mattis Hartwig: Efficient Query Answering in Nonparametric Probabilistic Graphical Models
    German Conference on Artificial Intelligence (Künstliche Intelligenz), 2020, Doctoral Consortium
    BibTeX
  • Marisa Mohr, Florian Wilhelm, Mattis Hartwig, Ralf Möller, Karsten Keller: New Approaches in Ordinal Pattern Representations for Multivariate Time Series
    in: Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), 2020, AAAI Press, Beach, Florida, USA, May 17-20, 2020, p.124-129
    DOI BibTeX
  • Mattis Hartwig, Marisa Mohr, Ralf Möller: Constructing Gaussian Processes for Probabilistic Graphical Models
    in: Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), 2020, AAAI, North Miami Beach, Florida, USA, May 17-20, 2020, p.57-62
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2019

  • Mattis Hartwig, Marcel Gehrke, Ralf Möller: Approximate Query Answering in Complex Gaussian Mixture Models
    in: Proceedings of the ICBK 2019, 2019, IEEE, p.81-86
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