Publications

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, and Nico Hoffmann. Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows. 2023. [PUMA: Bunch Distribution Electron Learning Zno imported] URL

Patrick Stiller, Varun Makdani, Franz Pöschel, Richard Pausch, Alexander Debus, Michael Bussmann, and Nico Hoffmann. Continual learning autoencoder training for a particle-in-cell simulation via streaming. 2022. [PUMA: Continual Zno autoencoder imported learning particle-in-cell simulation training] URL

Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, and Henning Wachsmuth. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Khalid Al-Khatib, Yufang Hou, and Manfred Stede (Eds.), 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, 184--189, Association for Computational Linguistics, November 2021. [PUMA: Analysis Argument Contrastive Extractive Key Learning Point Summarization Zno]

Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, and Sahar Vahdati. Unsupervised feature selection based on variance–covariance subspace distance. Neural Networks, (166):188-203, 2023. [PUMA: Feature Regularization Subspace Xack distance learning selection] URL

Markus Bauer, and Christoph Augenstein. Self-supervised learning in histopathology: New perspectives for prostate cancer grading. Lecture Notes in Computer Science, 348--360, Springer Nature Switzerland, Cham, 2024. [PUMA: Yaff cancer grading histopathology learning prostate self-supervised topic_lifescience]

Akshay Akshay, Masoud Abedi, Navid Shekarchizadeh, Fiona C Burkhard, Mitali Katoch, Alex Bigger-Allen, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. MLcps: machine learning cumulative performance score for classification problems. GigaScience, (12):giad108, December 2023. [PUMA: MLcps Xack Yaff cumulative learning machine performance] URL

Parvaneh Joharinad, and Jürgen Jost. Manifold learning, the scheme of Laplacian eigenmaps. Mathematics of Data, 227--251, Springer International Publishing, Cham, 2023. [PUMA: Laplacian Manifold eigenmaps learning]

Sascha Marton, Stefan Lüdtke, Christian Bartelt, and Heiner Stuckenschmidt. GradTree: Learning axis-aligned decision trees with gradient descent. 2023. [PUMA: GradTree Learning Zno axis-aligned decision descent gradient trees]

Niklas Deckers, and Martin Potthast. WARC-DL: Scalable Web Archive Processing for Deep Learning. 2022. [PUMA: Archive Deep Learning Processing Scalable WARC-DL Web Xack] URL

Ricardo Knauer, and Erik Rodner. Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, 114--129, Springer-Verlag, Berlin, Heidelberg, 2023. [PUMA: Zno best conic cost-sensitive interpretable learning machine meta-learning mixed-integer optimization selection subset]