Methods Beyond Supervised Learning

Our group’s research focuses on machine learning problems that move beyond needing large amounts of human annotation. Current projects include:


Name       Position
Dr. Mina Rezaei       Lead
Dr. Ludwig Bothmann       PostDoc
Dr. David Rügamer       Postdoc
Martin Binder       PhD Student
Emilio Dorigatti       PhD Student
Jann Goschenhofer       PhD Student
Hüseyin Anil Gündüz       PhD Student
Yawei Li       PhD Student
Xiao-Yin To       PhD Student


  1. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. arXiv preprint arXiv:2204.03342.
  2. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Cross-Modal Common Representation Learning with Triplet Loss Functions. arXiv preprint arXiv:2202.07901.
  3. Ott F, Rügamer D, Heublein L et al. (2022) Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. arXiv preprint arXiv:2202.07036.
  4. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2022) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2109.05232.
  5. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
    link | pdf
  6. Rezaei M, Soleymani F, Bischl B, Azizi S (2021) Deep Bregman Divergence for Contrastive Learning of Visual Representations. arXiv preprint arXiv:2109.07455.
  7. Rezaei M, Dorigatti E, Rügamer D, Bischl B (2021) Learning Statistical Representation with Joint Deep Embedded Clustering. arXiv preprint arXiv:2109.05232.
  8. Soleymani F, Eslami M, Elze T, Bischl B, Rezaei M (2021) Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images. arXiv preprint arXiv:2109.10777.