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Georg Schollmeyer
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2020 – today
- 2025
- [j9]Hannah Blocher
, Georg Schollmeyer:
Data depth functions for non-standard data by use of formal concept analysis. J. Multivar. Anal. 205: 105372 (2025) - 2024
- [j8]Hannah Blocher
Comparing machine learning algorithms by union-free generic depth. Int. J. Approx. Reason. 169: 109166 (2024) - [i10]Christoph Jansen, Georg Schollmeyer, Julian Rodemann, Hannah Blocher, Thomas Augustin:
Statistical Multicriteria Benchmarking via the GSD-Front. CoRR abs/2406.03924 (2024) - [i9]Julian Rodemann, Christoph Jansen, Georg Schollmeyer:
Reciprocal Learning. CoRR abs/2408.06257 (2024) - 2023
- [j7]Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin:
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. J. Mach. Learn. Res. 24: 231:1-231:37 (2023) - [j6]Krasymyr Tretiak
Neural network model for imprecise regression with interval dependent variables. Neural Networks 161: 550-564 (2023) - [c12]Hannah Blocher, Georg Schollmeyer, Christoph Jansen, Malte Nalenz:
Depth functions for partial orders with a descriptive analysis of machine learning algorithms. ISIPTA 2023: 59-71 - [c11]Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin:
In all likelihoods: robust selection of pseudo-labeled data. ISIPTA 2023: 412-425 - [c10]Christoph Jansen
Multi-target Decision Making Under Conditions of Severe Uncertainty. MDAI 2023: 45-57 - [c9]Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin:
Robust statistical comparison of random variables with locally varying scale of measurement. UAI 2023: 941-952 - [i8]Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin:
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning. CoRR abs/2303.01117 (2023) - [i7]Hannah Blocher, Georg Schollmeyer, Christoph Jansen, Malte Nalenz:
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms. CoRR abs/2304.09872 (2023) - [i6]Georg Schollmeyer, Hannah Blocher:
A note on the connectedness property of union-free generic sets of partial orders. CoRR abs/2304.10549 (2023) - [i5]Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin:
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement. CoRR abs/2306.12803 (2023) - [i4]Hannah Blocher, Georg Schollmeyer, Malte Nalenz, Christoph Jansen:
Comparing Machine Learning Algorithms by Union-Free Generic Depth. CoRR abs/2312.12839 (2023) - 2022
- [j5]Christoph Jansen
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty. Int. J. Approx. Reason. 144: 69-91 (2022) - [c8]Hannah Blocher, Georg Schollmeyer, Christoph Jansen
Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. IPMU (2) 2022: 17-30 - [i3]Christoph Jansen
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. CoRR abs/2209.01857 (2022) - [i2]Christoph Jansen
Multi-Target Decision Making under Conditions of Severe Uncertainty. CoRR abs/2212.06832 (2022) - 2021
- [c7]Dominik Kreiss, Georg Schollmeyer, Thomas Augustin:
Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge. ISIPTA 2021: 201-209 - [c6]Georg Schollmeyer:
Computing Simple Bounds for Regression Estimates for Linear Regression with Interval-valued Covariates. ISIPTA 2021: 273-279 - [i1]Christoph Jansen, Hannah Blocher, Thomas Augustin, Georg Schollmeyer:
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty. CoRR abs/2110.12879 (2021)
2010 – 2019
- 2019
- [c5]Cornelia Fuetterer
Constructing Simulation Data with Dependency Structure for Unreliable Single-Cell RNA-Sequencing Data Using Copulas. ISIPTA 2019: 216-224 - [c4]Georg Schollmeyer:
A Short Note on the Equivalence of the Ontic and the Epistemic View on Data Imprecision for the Case of Stochastic Dominance for Interval-Valued Data. ISIPTA 2019: 330-337 - 2018
- [j4]Christoph Jansen
Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences. Int. J. Approx. Reason. 98: 112-131 (2018) - [j3]Christoph Jansen
A probabilistic evaluation framework for preference aggregation reflecting group homogeneity. Math. Soc. Sci. 96: 49-62 (2018) - 2017
- [j2]Julia Plass, Marco E. G. V. Cattaneo
On the testability of coarsening assumptions: A hypothesis test for subgroup independence. Int. J. Approx. Reason. 90: 292-306 (2017) - [c3]Christoph Jansen
Decision Theory Meets Linear Optimization Beyond Computation. ECSQARU 2017: 329-339 - [c2]Christoph Jansen, Georg Schollmeyer, Thomas Augustin
Concepts for Decision Making under Severe Uncertainty with Partial Ordinal and Partial Cardinal Preferences. ISIPTA 2017: 181-192 - 2016
- [c1]Julia Plass, Marco E. G. V. Cattaneo, Georg Schollmeyer, Thomas Augustin:
Testing of Coarsening Mechanisms: Coarsening at Random Versus Subgroup Independence. SMPS 2016: 415-422 - 2015
- [j1]Georg Schollmeyer, Thomas Augustin:
Statistical modeling under partial identification: Distinguishing three types of identification regions in regression analysis with interval data. Int. J. Approx. Reason. 56: 224-248 (2015)
Coauthor Index
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