no code implementations • 6 Feb 2024 • Erik Weber, Jérôme Rutinowski, Markus Pauly
This paper tries to shed light on this, providing an in-depth analysis of the dark personality traits and conspiracy beliefs of GPT-3. 5 and GPT-4.
no code implementations • 17 Jan 2024 • Jakob Schwerter, Ketevan Gurtskaia, Andrés Romero, Birgit Zeyer-Gliozzo, Markus Pauly
However, the performance and validity are not completely understood, particularly compared to the standard MICE PMM.
no code implementations • 14 Apr 2023 • Jérôme Rutinowski, Sven Franke, Jan Endendyk, Ina Dormuth, Markus Pauly
In addition, ChatGPT's Big Five personality traits were tested using the OCEAN test and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test.
no code implementations • 14 Mar 2023 • Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller, Markus Pauly, Daniel Horn
We propose a general type of test data and examine all methods in a simulation study.
no code implementations • 13 Mar 2023 • Lena Schmid, Moritz Roidl, Markus Pauly
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors.
no code implementations • 19 Jan 2023 • Nilah Ravi Nair, Lena Schmid, Fernando Moya Rueda, Markus Pauly, Gernot A. Fink, Christopher Reining
It is unknown what physical characteristics and/or soft-biometrics, such as age, height, and weight, need to be taken into account to train a classifier to achieve robustness towards heterogeneous populations in the training and testing data.
no code implementations • 26 Oct 2022 • Maximilian Kertel, Stefan Harmeling, Markus Pauly
Many production processes are characterized by numerous and complex cause-and-effect relationships.
no code implementations • 14 Jan 2022 • Maximilian Kertel, Markus Pauly
In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data.
no code implementations • 14 Jan 2022 • Lena Schmid, Alexander Gerharz, Andreas Groll, Markus Pauly
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods.
no code implementations • 23 Dec 2021 • Philip Buczak, Andreas Groll, Markus Pauly, Jakob Rehof, Daniel Horn
Hyperparameter tuning is one of the the most time-consuming parts in machine learning.
no code implementations • 9 Dec 2021 • Burim Ramosaj, Justus Tulowietzki, Markus Pauly
In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction are used.
no code implementations • 13 Sep 2020 • Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland, Adalbert Wilhelm, Tim Friede
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion.
1 code implementation • 28 May 2020 • He Huang, Martin Pouls, Anne Meyer, Markus Pauly
The computational results show that the addition of this routing data can be beneficial to the model performance.
no code implementations • 5 Dec 2019 • Burim Ramosaj, Markus Pauly
Due to its intuitive idea and flexible usage, it is important to explore circumstances, for which the permutation importance based on Random Forest correctly indicates informative covariates.
1 code implementation • 30 Nov 2017 • Burim Ramosaj, Markus Pauly
In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5. 0 algorithm or modified random forest procedures.