Publications

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

Ricardo Knauer, und 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]

Sophie Adama, Shang-Ju Wu, Nicoletta Nicolaou, und Martin Bogdan. Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning. SNE Simul. Notes Eur., (32)1:37--45, ARGESIM Arbeitsgemeinschaft Simulation News, 2022. [PUMA: Zno conscious hybrid learning machine patient states {CLIS}]

Oliver Kirsten, Martin Bogdan, und Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]

Aruscha Kramm, Eric Peukert, André Ludwig, und Bogdan Franczyk. Machine Learning Based Mobile Capacity Estimation for Roadside Parking. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (10):99--106, Copernicus GmbH, 2024. [PUMA: Based Capacity Estimation Learning Machine Mobile Parking Roadside yaff]

Maria Carolina Novitasari, Johannes Quaas, und Miguel R. D. Rodrigues. Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning. Environmental Data Science, (3)Cambridge University Press (CUP), 2024. [PUMA: autoconversion forecasting learning machine rates satellite yaff] URL

Tom Richard Vargis, und Siavash Ghiasvand. A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, Januar 2022. [PUMA: Cluster Computer Computing, Distributed, Learning Machine Parallel, Science and myOwn] URL

Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji, Miguel D. Mahecha, und Gustau Camps-Valls. Explainable Earth Surface Forecasting under Extreme Events. arXiv, 2024. [PUMA: (cs.LG), Computer FOS: Learning Machine and information sciences sciences, topic_earthenvironment] URL

Lucas Lange, Maurice-Maximilian Heykeroth, und Erhard Rahm. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: area_responsibleai area_bigdata (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and ep information sciences]

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, und Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

Christopher Klapproth, Rituparno Sen, Peter F Stadler, Sven Findeiß, und Jörg Fallmann. Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, Dezember 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]