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Publikationen:

• Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme (2024):
  ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing, in arXiv:2403.03812.
• Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme (2024):
  Moco: A Learnable Meta Optimizer for Combinatorial Optimization, in arXiv:2402.04915.
• Tobias Hille, Maximilian Stubbemann, Tom Hanika (2024):
  Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, in arxiv:2403.08438.
• Maximilian Stubbemann, Tobias Hille, Tom Hanika (2023):
  Selecting Features by their Resilience to the Curse of Dimensionality, in arXiv:2304.02455.
• Maximilian Stubbemann, Gerd Stumme (2023):
  The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks, in Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD.
• Maximilian Stubbemann, Tom Hanika, Friedirch Martin Schneider (2023):
  Intrinsic Dimension for Large-Scale Geometric Learning, in Transactions on Machine Learning Research.
• Maximilian Stubbemann, Gerd Stumme (2022):
  LG4AV: Combining Language Models and Graph Neural Networks for Author Verification, in Advances in Intelligent Data Analysis XX.
• Dominik Duerrschnabel, Maximilian Stubbemann, Tom Hanika (2022):
  FCA2VEC: Embedding Techniques for Formal Concept Analysis, in Complex Data Analytics with Formal Concept Analysis.
• Tobias Koopmann, Maximilian Stubbemann, Matthias Kapa, Michael Paris, Guido Buenstorf, Tom Hanika, Andreas Hotho, Robert Jaeschke, Gerd Stumme (2021):
  Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research, in Scientometrics.
• Maximilian Stubbemann, Tom Hanika, Gerd Stumme (2022):
  Orometric Methods in Bounded Metric Data, in Advances in Intelligent Data Analysis XVIII.