| Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences C Jansen, G Schollmeyer, T Augustin International Journal of Approximate Reasoning 98, 112–131, 2018 | 43 | 2018 |
| Quantifying Degrees of E-admissibility in Decision Making with Imprecise Probabilities C Jansen, G Schollmeyer, T Augustin Reflections on the Foundations of Probability and Statistics: Essays in …, 2022 | 11 | 2022 |
| Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty C Jansen, H Blocher, T Augustin, G Schollmeyer International Journal of Approximate Reasoning, 2022 | 11 | 2022 |
| Decision theory meets linear optimization beyond computation C Jansen, T Augustin, G Schollmeyer Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th …, 2017 | 9 | 2017 |
| Depth functions for partial orders with a descriptive analysis of machine learning algorithms H Blocher, G Schollmeyer, C Jansen, M Nalenz International Symposium on Imprecise Probability: Theories and Applications …, 2023 | 8 | 2023 |
| Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement C Jansen, G Schollmeyer, H Blocher, J Rodemann, T Augustin arXiv preprint arXiv:2306.12803, 2023 | 7 | 2023 |
| Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement C Jansen, G Schollmeyer, H Blocher, J Rodemann, T Augustin Uncertainty in Artificial Intelligence (UAI 2023) 216, 941-952, 2023 | 7 | 2023 |
| Statistical Comparisons of Classifiers by Generalized Stochastic Dominance C Jansen, M Nalenz, G Schollmeyer, T Augustin Journal of Machine Learning Research 24, 1-37, 2022 | 7 | 2022 |
| Statistical models for partial orders based on data depth and formal concept analysis H Blocher, G Schollmeyer, C Jansen International Conference on Information Processing and Management of …, 2022 | 6 | 2022 |
| Detecting stochastic dominance for poset-valued random variables as an example of linear programming on closure systems G Schollmeyer, C Jansen, T Augustin | 6 | 2017 |
| A probabilistic evaluation framework for preference aggregation reflecting group homogeneity C Jansen, G Schollmeyer, T Augustin Mathematical Social Sciences 96, 49-62, 2018 | 5 | 2018 |
| Multi-target decision making under conditions of severe uncertainty C Jansen, G Schollmeyer, T Augustin International Conference on Modeling Decisions for Artificial Intelligence …, 2023 | 4 | 2023 |
| In all likelihoods: Robust selection of pseudo-labeled data J Rodemann, C Jansen, G Schollmeyer, T Augustin International Symposium on Imprecise Probability: Theories and Applications …, 2023 | 3 | 2023 |
| Decision making with state-dependent preference systems C Jansen, T Augustin International Conference on Information Processing and Management of …, 2022 | 2 | 2022 |
| Updated network analysis of the imprecise probability community based on ISIPTA electronic proceedings G Walter, C Jansen, T Augustin Poster for ISIPTA 15, 2015 | 2 | 2015 |
| Some contributions to decision making in complex information settings with imprecise probabilities and incomplete preferences C Jansen lmu, 2018 | 1 | 2018 |
| Comparing machine learning algorithms by union-free generic depth H Blocher, G Schollmeyer, M Nalenz, C Jansen International Journal of Approximate Reasoning 169, 109166, 2024 | | 2024 |
| In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning J Rodemann, C Jansen, G Schollmeyer, T Augustin arXiv preprint arXiv:2303.01117, 2023 | | 2023 |
| Decision Making under Complex Information with Applications to Statistics and Machine Learning C Jansen https://www.researchgate.net/publication …, 2022 | | 2022 |
| Elicitation of Preference Systems: Two Procedures and their Application to Decision Making under Severe Uncertainty C Jansen, G Schollmeyer, T Augustin The Twelfth International Symposium on Imprecise Probability: Theories and …, 2021 | | 2021 |
Georg SchollmeyerInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
