Hannah Blocher

I started as a PhD student at the Institute of Statistics in February 2021. I obtained a Bachelor's Degree (B.A.) in Mathematics form the LMU Munich and a Master's Degree (M.Sc) ins Statistics form the LMU Munich

My current research focus is on defining data depth functions for non-standard data, like rankings, with theory of formal concept analysis. With this we define statistical models and tests.

Research Interests:

• Data Depth Functions
• Formal Concept Analysis
• Partial Orders
• (Spatial Statistics, Decision Theory)

Articles

• Hannah Blocher, Georg Schollmeyer (2023): Data depth functions for non-standard data by use of formal concept analysis. Journal of Multivariate Analysis, 205: 105372. [link (last accessed: 28.03.2025)]
• H. Blocher, G. Schollmeyer (2024): Union-free Generic Depth for Non-standard Data.
  ArXiv:2412.14745. https://arxiv.org/abs/2412.14745 (last accessed: 28.03.2025).
• E. Arias, H. Blocher, J. Rodemann, M. Li, C. Heumann, M. Aßenmacher (2024):
  Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework. ArXiv:2410.18653. https://arxiv.org/abs/2410.18653 (last accessed: 28.03.2025).
• Christoph Jansen, Georg Schollmeyer, Julian Rodemann, Hannah Blocher and Thomas Augustin (2024):
  Statistical Multicriteria Benchmarking via the GSD-Front. 38th Conference on Neural Information Processing System (NeurIPS 2024), Vancouver: OpenReview.net [link - on conference site (last accessed: 28.03.2025)]
• Julian Rodemann*, Hannah Blocher* (2024): Partial Rankings of Optimizers. International Conference on Learning Representations ICLR 2024, Tiny Papers Track, Vienna, Austria. [link, poster: see "Downloads" below, * equal contribution (last accessed: 28.03.2025)]
• 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 (last accessed: 28.03.2025)]
• 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 (last accessed: 28.03.2025)]
• Georg Schollmeyer and Hannah Blocher (2023) A note on the connectedness property of union-free generic sets of partial orders. URL https://arxiv.org/abs/2304.10549 (last accessed: 28.03.2025).
• Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann and Thomas Augustin (2023): Robust statistical comparison of random variables with locally varying scale of measurement. Uncertainty in Artificial Intelligence (UAI 2023). PMLR. [link, preprint (last accessed: 28.03.2025)]
• 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 (last accessed: 28.03.2025), preprint: see "Downloads" below]

• 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, preprint (last accessed: 28.03.2025)]

Presentations

• Depth Functions using Formal Concept Analysis (at Department of Probability and Mathematical Statistics, Charles University)
• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at the Research Methods and Statistics Group, Institute of Psychology, University of Hamburg) [slides: see "Downloads" below]
• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at the Knowledge & Data Engineering Group (KDE), EECS, University of Kassel) [slides: see "Downloads" below]
• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at ISIPTA 2023) [slides: see "Downloads" below]
• Statistical Models for Partial Orders based on Data Depth and Formal Concept Analysis (at IPMU 2022) [slides: see "Downloads" below]
• Statistical Models for Partial Orders based on Data Depth and Formal Concept Analysis (at DAGStat 2022) [slides: see "Downloads" below]
• A Statistical Depth Function for Non-Standard Data based on Formal Concept Analysis (at ECDA 2021) [slides: see "Downloads" below]

Teaching

• WS 2024/25: Seminar on statistical analysis of areal data, spatial point processes and related topics (Bachelor and Master)
• WS 2023/24: Seminar on statistical analysis of areal data, spatial point processes and related topics (Bachelor and Master)
• SS 2022: Methoden der lineare Algebra in der Statistik (Bachelor)
• SS 2022: Einführung in die Wahrscheinlichkeitsrechnung und in die induktive Statistik (Bachelor)
• WS 2021/22: Statistical Inference (Master)
• WS 2021/22: Measure and Probability Theory (Master)
• WS 2021/22: CoVorMa - Coronafester Vorkurs für Mathefrustrierte (offen für alle LMU Studierende)
• SS 2021: Einführung in die Wahrscheinlichkeitsrechnung und in die induktive Statistik (Bachelor)
• SS 2021: CoVorMa - Coronafester Vorkurs für Mathefrustrierte (offen für alle LMU Studierende)