The Minimal Degrees of Faithful Representations of the Sporadic Simple Groups and their Covering Groups. - Christoph Jansen (LMS J. Comput. Math., 2005)

Multicenter Data Sharing for Collaboration in Sleep Medicine. - Maximilian Beier; Christoph Jansen; Geert Mayer; Thomas Penzel; Andrea Rodenbeck; René Siewert; Jie Wu 014; Dagmar Krefting (CCGRID, 2015)

Reconstructing Missing Areas in Facial Images. - Christoph Jansen; Radek Mackowiak; Nico Hezel; Moritz Ufer; Gregor Altstadt; Kai Uwe Barthel (ISM, 2015)

Extending XNAT towards a Cloud-based Quality Assessment Platform for Retinal Optical Coherence Tomographies. - Christoph Jansen; Maximilian Beier; Michael Witt 001; Jie Wu 014; Dagmar Krefting (Scalable Comput. Pract. Exp., 2015)

Employing Docker Swarm on OpenStack for Biomedical Analysis. - Christoph Jansen; Michael Witt 001; Dagmar Krefting (ICCSA (2), 2016)

Fine-grained Supervision and Restriction of Biomedical Applications in Linux Containers. - Michael Witt 001; Christoph Jansen; Dagmar Krefting; Achim Streit (CCGrid, 2017)

Decision Theory Meets Linear Optimization Beyond Computation. - Christoph Jansen; Thomas Augustin 001; Georg Schollmeyer (ECSQARU, 2017)

Towards Reproducible Research in a Biomedical Collaboration Platform following the FAIR Guiding Principles. - Christoph Jansen; Maximilian Beier; Michael Witt 001; Sonja Frey; Dagmar Krefting (UCC (Companion"), 2017)

Multicenter data sharing for collaboration in sleep medicine. - Maximilian Beier; Christoph Jansen; Geert Mayer; Thomas Penzel; Andrea Rodenbeck; René Siewert; Michael Witt 001; Jie Wu 014; Dagmar Krefting (Future Gener. Comput. Syst., 2017)

Mitigating risks of digitalization through managed industrial security services. - Christoph Jansen; Sabina Jeschke (AI Soc., 2018)

Sandboxing of biomedical applications in Linux containers based on system call evaluation. - Michael Witt 001; Christoph Jansen; Dagmar Krefting; Achim Streit (Concurr. Comput. Pract. Exp., 2018)

Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences. - Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (Int. J. Approx. Reason., 2018)

A probabilistic evaluation framework for preference aggregation reflecting group homogeneity. - Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (Math. Soc. Sci., 2018)

Reproducibility and Performance of Deep Learning Applications for Cancer Detection in Pathological Images. - Christoph Jansen; Bruno Schilling; Klaus Strohmenger; Michael Witt 001; Jonas Annuscheit; Dagmar Krefting (CCGRID, 2019)

Curious Containers: A framework for computational reproducibility in life sciences with support for Deep Learning applications. - Christoph Jansen; Jonas Annuscheit; Bruno Schilling; Klaus Strohmenger; Michael Witt 001; Felix Bartusch; Christian Herta; Peter Hufnagl; Dagmar Krefting (Future Gener. Comput. Syst., 2020)

IIoT and cyber-resilience. - Sebastian Gajek; Michael Lees; Christoph Jansen (AI Soc., 2021)

The EMPAIA Platform: Vendor-neutral integration of AI applications into digital pathology infrastructures. - Christoph Jansen; Klaus Strohmenger; Daniel Romberg; Tobias Küster; Nick Weiss; Björn Lindequist; Michael Franz; André Homeyer; Norman Zerbe (CCGRID, 2022)

Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. - Hannah Blocher; Georg Schollmeyer; Christoph Jansen (IPMU (2), 2022)

Decision Making with State-Dependent Preference Systems. - Christoph Jansen; Thomas Augustin 001 (IPMU (1), 2022)

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology. - Daniel Romberg; Klaus Strohmenger; Christoph Jansen; Tobias Küster; Nick Weiss; Christian Geißler; Tomasz Soltysinski; Michael Takla; Peter Hufnagl; Norman Zerbe; André Homeyer (Comput. Methods Programs Biomed., 2022)

Anonymization of Whole Slide Images in Histopathology for Research and Education. - Tom Bisson; Michael Franz; Isil Dogan O; Daniel Romberg; Christoph Jansen; Peter Hufnagl; Norman Zerbe (CoRR, 2022)

Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty. - Christoph Jansen; Hannah Blocher; Thomas Augustin 001; Georg Schollmeyer (Int. J. Approx. Reason., 2022)

Depth functions for partial orders with a descriptive analysis of machine learning algorithms. - Hannah Blocher; Georg Schollmeyer; Christoph Jansen; Malte Nalenz (ISIPTA, 2023)

In all likelihoods: robust selection of pseudo-labeled data. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (ISIPTA, 2023)

Multi-target Decision Making Under Conditions of Severe Uncertainty. - Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (MDAI, 2023)

Robust statistical comparison of random variables with locally varying scale of measurement. - Christoph Jansen; Georg Schollmeyer; Hannah Blocher; Julian Rodemann; Thomas Augustin 001 (UAI, 2023)

In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (CoRR, 2023)

The vendor-agnostic EMPAIA platform for integrating AI applications into digital pathology infrastructures. - Christoph Jansen; Björn Lindequist; Klaus Strohmenger; Daniel Romberg; Tobias Küster; Nick Weiss; Michael Franz; Lars Ole Schwen; Theodore Evans; André Homeyer; Norman Zerbe (Future Gener. Comput. Syst., 2023)

Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. - Christoph Jansen; Malte Nalenz; Georg Schollmeyer; Thomas Augustin 001 (J. Mach. Learn. Res., 2023)

Statistical Multicriteria Benchmarking via the GSD-Front. - Christoph Jansen; Georg Schollmeyer; Julian Rodemann; Hannah Blocher; Thomas Augustin 001 (NeurIPS, 2024)

Reciprocal Learning. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer (NeurIPS, 2024)

Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative. - Norman Zerbe; Lars Ole Schwen; Christian Geißler; Katja Wiesemann; Tom Bisson; Peter Boor; Rita Carvalho; Michael Franz; Christoph Jansen; Tim-Rasmus Kiehl; Björn Lindequist; Nora Charlotte Pohlan; et al. (CoRR, 2024)

Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision. - Stefan Dietrich; Julian Rodemann; Christoph Jansen (CoRR, 2024)

Comparing machine learning algorithms by union-free generic depth. - Hannah Blocher; Georg Schollmeyer; Malte Nalenz; Christoph Jansen (Int. J. Approx. Reason., 2024)

A systematic review and meta analysis on digital mental health interventions in inpatient settings. - Alexander Diel; Isabel Carolin Schröter; Anna-Lena Frewer; Christoph Jansen; Anita Robitzsch; Gertraud Gradl-Dietsch; Martin Teufel; Alexander Bäuerle (npj Digit. Medicine, 2024)

Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information. - Christoph Jansen (CoRR, 2025)

A Statistical Case Against Empirical Human-AI Alignment. - Julian Rodemann; Esteban Garces Arias; Christoph Luther; Christoph Jansen; Thomas Augustin 001 (CoRR, 2025)

Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation. - Polina Gordienko; Christoph Jansen; Thomas Augustin 001; Martin Rechenauer (CoRR, 2025)

Statistical Multicriteria Evaluation of LLM-Generated Text. - Esteban Garces Arias; Hannah Blocher; Julian Rodemann; Matthias Aßenmacher; Christoph Jansen (CoRR, 2025)