WORK IN PROGRESS:
- Reciprocal Learning (with Julian Rodemann, Georg Schollmeyer and Thomas Augustin).
- Starshaped Subgroup Discovery with Uniform Generalization Guarantees (with Georg Schollmeyer and Julian Rodemann).
- Stefan Dietrich, Julian Rodemann and Christoph Jansen (2024): Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision. Forthcoming in: Combining, modelling and analyzing imprecision, randomness and dependence. Advances in Intelligent Systems and Computing. Springer.
- Hannah Blocher, Georg Schollmeyer, Malte Nalenz and Christoph Jansen (2024): Comparing Machine Learning Algorithms by Union-Free Generic Depth. International Journal of Approximate Reasoning, 169: 1-23.
[link] - Christoph Jansen, Malte Nalenz, Georg Schollmeyer and Thomas Augustin (2023): Statistical comparisons of classifiers by generalized stochastic dominance. Journal of Machine Learning Research, 24: 1 - 37.
[link, research gate] - Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann and Thomas Augustin (2023): Robust statistical comparison of random variables with locally varying scale of measurement. In: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023). Proceedings of Machine Learning Research, vol. 216. PMLR.
[link, poster, video teaser]
- Julian Rodemann, Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2023): In All Likelihoods: Robust Selection of Pseudo-Labeled Data. In: Proceedings of the Thirteenth International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '23). Proceedings of Machine Learning Research, vol. 215. PMLR.
[link, preprint]
- Hannah Blocher, Georg Schollmeyer, Christoph Jansen and Malte Nalenz (2023): Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms. In: Proceedings of the Thirteenth International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '23). Proceedings of Machine Learning Research, vol. 215. PMLR.
[link, preprint]
- Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2023): Multi-target decision making under conditions of severe uncertainty. Forthcoming in: Torra, V.; Y. Narukawa (eds): Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science, vol 13890. Springer.
[link, preprint, research gate] - Christoph Jansen and Thomas Augustin (2022): Decision making with state-dependent preference systems. In: Ciucci, D.; Couso, I.; Medina, J.; Slezak, D.; Petturiti, D.; Bouchon-Meunier, B.; Yager, R.R. (eds): Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Science, vol 1601, Springer.
[link, preprint, research gate] - Hannah Blocher, Georg Schollmeyer and Christoph Jansen (2022): Statistical models for partial orders based on data depth and formal concept analysis. In: Ciucci, D.; Couso, I.; Medina, J.; Slezak, D.; Petturiti, D.; Bouchon-Meunier, B.; Yager, R.R. (eds): Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Science, vol 1602, Springer.
[link, preprint, research gate] - Christoph Jansen, Hannah Blocher, Thomas Augustin and Georg Schollmeyer (2022): Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty. International Journal of Approximate Reasoning, 144 : 69 - 91.
[link, early poster version, preprint, research gate] - Jean Baccelli, Georg Schollmeyer and Christoph Jansen (2021): Risk aversion over finite domains. Theory and Decision, 93(3): 371 - 397.
[link, preprint, research gate] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2022): Quantifying Degrees of E-Admissibility in Decision Making with Imprecise Probabilities. In: T. Augustin, F. Cozman, and G. Wheeler (Eds.), Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld, Theory and Decision Library A, vol 54. Springer.
[link, preprint, research gate] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2018): Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences. International Journal of Approximate Reasoning, 98: 112 - 131.
[link, preprint, research gate] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2018): A probabilistic evaluation framework for preference aggregation reflecting group homogeneity. Mathematical Social Sciences, 96: 49-62.
[link, preprint, research gate] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2017): Decision theory meets linear optimization beyond computation. In: Antonucci, A.; Cholvy, L.; Papini, O. (eds): Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science, vol 10369. Springer.
[link, early poster version, research gate] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2017): Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences. In: Antonucci, A.; Corani, G.; Couso, I.; Destercke, S. (eds): Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications. Proceedings of Machine Learning Research, vol 62. PMLR.
[link, research gate]
TECHNICAL REPORTS AND PREPRINTS:
- Christoph, Georg Schollmeyer, Julian Rodemann, Hannah Blocher and Thomas Augustin (2024): Statistical Multicriteria Benchmarking via the GSD-Front.
[preprint]
- Hannah Blocher, Georg Schollmeyer, Malte Nalenz and Christoph Jansen (2023): Comparing Machine Learning Algorithms by Union-Free Generic Depth.
[preprint] - Georg Schollmeyer, Christoph Jansen and Thomas Augustin (2017): A simple descriptive method for multidimensional item response theory based on stochastic dominance. Technical Report 210, Department of Statistics, LMU Munich.
[link] - Georg Schollmeyer, Christoph Jansen and Thomas Augustin (2017): Detecting stochastic dominance for poset-valued random variables as an example of linear programming on closure systems. Technical Report 209, Department of Statistics, LMU Munich.
[link] - Christoph Jansen, Georg Schollmeyer and Thomas Augustin (2016): Probabilistic Evaluation of Preference Aggregation Functions: A Statistical Approach in Social Choice Theory. Technical Report 193, Department of Statistics, LMU Munich.
[link]
OTHER PUBLICATIONS:
- Georg Schollmeyer, Hannah Blocher, Christoph Jansen and Thomas Augustin (2023): On the analysis of epiontic data: a case study. Poster at ISIPTA '23.
[abstract] - Christoph Jansen and Georg Schollmeyer (2018): What’s hot in Mathematical Philosophy? The Reasoner, 12(9): 73 - 74.
[link] - Christoph Jansen (2016): Report on MuST 2016. The Reasoner, 10(6): 47 - 48.
[link]
THESES:
- Christoph Jansen (2018): Some contributions to decision making in complex information settings with imprecise probabilities and incomplete preferences: Theoretical and algorithmic results. Doctoral dissertation, Department of Statistics, LMU Munich.
[link] - Christoph Jansen (2015): Decision making under partial information using precise and imprecise probabilistic models. Master's thesis, Department of Statistics, LMU Munich.
[link] - Christoph Jansen (2013): Boolesche Algebren und der Stonesche Darstellungssatz (in German). Bachelor's thesis, Department of Mathematics, LMU Munich.
SELECTED (UPCOMING) CONFERENCES:
- 1st Workshop on machine learning under weakly structured information, May 2023, Munich
Multcriteria benchmarking via the GSD-front
Invited talk
- SIPTA Virtual Seminar, April 2024
Decision making under weakly structured information with applications to robust statistics and machine learning
Invited talk
[slides] - UAI 2023, August 2023, Pittsburgh, USA
Robust statistical comparison of random variables with locally varying scale of measurement (with G. Schollmeyer, H. Blocher, J. Rodemann and T. Augustin)
[link] - ISIPTA 2023, July 2023, Oviedo, Spain
Depth function for partial orders with a descriptive analysis of machine learning algorithms (with H. Blocher, G. Schollmeyer and M. Nalenz)
[link]
In all likelihoods: Robust selection of pseudo-labeled data (with J. Rodemann, G. Schollmeyer and T. Augustin)
[link]
On the analysis of epiontic data: A case-study (with G. Schollmeyer, H. Blocher and T. Augustin)
[link]
- MDAI 2023, June 2023, Umea, Sweden
Multi-target decision making under conditions of severe uncertainty (with G. Schollmeyer and T. Augustin)
[slides]
- Machine Learning meets Mathematical Philosophy, June 2023
ML und weakly structured information: A decision-theoretic perspective
[slides] - Institutskolloquium, Institut für Statistik der LMU, Oktober 2022
Decision Making under Complex Information with Applications to Statistics and Machine Learning
[slides] - IPMU'22, Milano
Decision making with state-dependent preference systems (with T. Augustin)
[slides]
Statistical models for partial orders based on data depth and formal concept analysis (with H. Blocher and G. Schollmeyer)
[slides] - IFORS ’21, Seoul
Methods for eliciting preference systems with applications to decision making under severe uncertainty (with G. Schollmeyer and T. Augustin)
[slides] - ISIPTA ’21, Granada
Elicitation of preference systems: Two procedures and their application to decision making under severe uncertainty (with G. Schollmeyer and T. Augustin)
[abstract, poster] - ECSQARU ’17, Lugano
Decision theory meets linear optimization (beyond computation) (with G. Schollmeyer and T. Augustin)
[slides] - ISIPTA ’17, Lugano
Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences (with G. Schollmeyer and T. Augustin)
[slides] - PROGIC 2017, München
Decision making under severe uncertainty with partial ordinal and partial cardinal preferences (with G. Schollmeyer)
[link] - Sommerklausur Statistisches Institut 2016, Holzhausen
Probabilistic evaluation of preference aggregation functions (with T. Augustin) - MuST 2016, München
Probabilistic evaluation of preference aggregation functions (with T. Augustin)
[conference report] - WPMSIIP 2015, München
Gamma-Maximin and least favorable prior distributions
[link] - Statistische Woche 2015, Hamburg
Statistical network analysis of the international arms trade (with G. Kauermann, P. Thurner and C. Schmid)
[abstract] - ISIPTA 2015, Pescara
Decision theory meets linear optimization beyond computation (with T. Augustin)
[poster]
Updated network analysis of the imprecise probability community based on ISIPTA proceedings (with G. Walter and T. Augustin)
[poster] - Workshop Introduction to Exponential Random Graph Models, 2014, Universität Zürich
- SIPTA Summer School 2014, Montpellier
[link]