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

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)

A note on the connectedness property of union-free generic sets of partial orders. - Georg Schollmeyer; Hannah Blocher (CoRR, 2023)

Partial Rankings of Optimizers. - Julian Rodemann; Hannah Blocher (Tiny Papers @ ICLR, 2024)

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

Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework. - Esteban Garces Arias; Hannah Blocher; Julian Rodemann; Meimingwei Li; Christian Heumann; Matthias Aßenmacher (CoRR, 2024)

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

Data depth functions for non-standard data by use of formal concept analysis. - Hannah Blocher; Georg Schollmeyer (J. Multivar. Anal., 2025)

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