Barbara Hammer

Barbara Hammer

bhammer@techfak.uni-bielefeld.de
Institution: Bielefeld University
Department: Machine Learning Group
Position: Head of the Hammer Lab

Sustainable AI, Hybrid Systems, Computational Learning Theory, Applications for Critical Infrastructure

Incremental learning and learning with drift, learning from limited data set, learning with label noise or few labels, reliability of learning, efficient deep learning, fairness of ML; explainable ML, readability of learning, learning with structured data, prototype-based models, graph neural networks, recurrent and recursive models; biomedical applications

  • Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles (2024)

    Authors: Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier

    Published at: Proceedings of the AAAI Conference on Artificial Intelligence (Volume: 38)
    DOI: 10.1609/aaai.v38i13.29352

  • Physics-Informed Graph Neural Networks forWater Distribution Systems (2024)

    Authors: Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer

    Published at: Proceedings of the AAAI Conference on Artificial Intelligence (Volume: 38)
    DOI: 10.1609/aaai.v38i20.30192

  • A Remark on Concept Drift for Dependent Data (2024)

    Authors: Fabian Hinder, Valerie Vaquet, Barbara Hammer

    Published at: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Volume: 14641 LNCS)
    DOI: 10.1007/978-3-031-58547-0_7

  • Semantic Properties of Cosine Based Bias Scores for Word Embeddings (2024)

    Authors: Sarah Schröder, Alexander Schulz, Fabian Hinder, Barbara Hammer

    Published at: International Conference on Pattern Recognition Applications and Methods (Volume: 1)
    DOI: 10.5220/0012577200003654

  • Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks (2024)

    Authors: Valerie Vaquet, Fabian Hinder, Barbara Hammer

    Published at: International Conference on Pattern Recognition Applications and Methods (Volume: 1)
    DOI: 10.5220/0012361200003654

  • Foundation Model Vision Transformers are Great Tracking Backbones (2024)

    Authors: Tristan Kenneweg, Philp Kenneweg, Barbara Hammer

    Published at: International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
    DOI: 10.1109/ACDSA59508.2024.10467598

  • Improving Line Search Methods for Large Scale Neural Network Training (2024)

    Authors: Philip Kenneweg, Tristan Kenneweg, Barbara Hammer

    Published at: International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
    DOI: 10.1109/ACDSA59508.2024.10467724

  • Incremental permutation feature importance (iPFI): towards online explanations on data streams (2023)

    Authors: Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer

    Published at: Machine Learning (Volume: 112)
    DOI: 10.1007/s10994-023-06385-y

  • “I do not know! but why?” — Local model-agnostic example-based explanations of reject (2023)

    Authors: André Artelt, Roel Visser, Barbara Hammer

    Published at: Neurocomputing (Volume: 558)
    DOI: 10.1016/j.neucom.2023.126722

  • Model-based explanations of concept drift (2023)

    Authors: Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer

    Published at: Neurocomputing (Volume: 555)
    DOI: 10.1016/j.neucom.2023.126640