@book{WendlerGroettrup2016, author = {Wendler, Tilo and Gr{\"o}ttrup, S{\"o}ren}, title = {Data Mining with SPSS Modeler}, subtitle = {Theory, Exercises and Solutions}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-28709-6}, doi = {https://doi.org/10.1007/978-3-319-28709-6}, pages = {XII, 1059}, year = {2016}, language = {en} } @book{WendlerGroettrup2021, author = {Wendler, Tilo and Gr{\"o}ttrup, S{\"o}ren}, title = {Data Mining with SPSS Modeler}, subtitle = {Theory, Exercises and Solutions}, edition = {2nd edition}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-54338-9}, doi = {https://doi.org/10.1007/978-3-030-54338-9}, pages = {XV, 1274}, year = {2021}, language = {en} } @article{AlsmeyerGroettrup2015, author = {Alsmeyer, Gerold and Gr{\"o}ttrup, S{\"o}ren}, title = {Branching within branching: A model for host-parasite co-evolution}, volume = {126}, journal = {Stochastic Processes and their Applications}, number = {6}, publisher = {Elsevier}, address = {Amsterdam}, doi = {https://doi.org/10.1016/j.spa.2015.12.007}, pages = {1839 -- 1883}, year = {2015}, language = {en} } @article{AlsmeyerGroettrup2013, author = {Alsmeyer, Gerold and Gr{\"o}ttrup, S{\"o}ren}, title = {A host-parasite model for a two-type cell population}, volume = {45}, journal = {Advances in Applied Probability}, number = {3}, publisher = {Cambridge University Press}, address = {Cambridge}, issn = {1475-6064}, doi = {https://doi.org/10.1239/aap/1377868536}, pages = {719 -- 741}, year = {2013}, language = {en} } @unpublished{AlsmeyerGroettrup2015, author = {Alsmeyer, Gerold and Gr{\"o}ttrup, S{\"o}ren}, title = {Branching within branching I: The extinction problem}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1505.03979}, year = {2015}, language = {en} } @unpublished{AlsmeyerGroettrup2015, author = {Alsmeyer, Gerold and Gr{\"o}ttrup, S{\"o}ren}, title = {Branching within branching II: Limit theorems}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1505.03981}, year = {2015}, language = {en} } @inproceedings{HartungMallickGroettrupetal.2024, author = {Hartung, Kai and Mallick, Sambit and Gr{\"o}ttrup, S{\"o}ren and Georges, Munir}, title = {Evaluation Metrics in LLM Code Generation}, booktitle = {Text, Speech, and Dialogue: 27th International Conference, TSD 2024 Brno, Czech Republic, September 9-13, 2024 Proceedings, Part I}, editor = {N{\"o}th, Elmar and Hor{\´a}k, Aleš and Sojka, Petr}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-70563-2}, doi = {https://doi.org/10.1007/978-3-031-70563-2_17}, pages = {214 -- 226}, year = {2024}, language = {en} } @inproceedings{HartungHerygersKurlekaretal.2023, author = {Hartung, Kai and Herygers, Aaricia and Kurlekar, Shubham Vijay and Zakaria, Khabbab and Volkan, Taylan and Gr{\"o}ttrup, S{\"o}ren and Georges, Munir}, title = {Measuring Sentiment Bias in Machine Translation}, booktitle = {Text, Speech, and Dialogue: 26th International Conference: Proceedings}, editor = {Ekštein, Kamil and P{\´a}rtl, František and Konop{\´i}k, Miloslav}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-40498-6}, issn = {1611-3349}, doi = {https://doi.org/10.1007/978-3-031-40498-6_8}, pages = {82 -- 93}, year = {2023}, language = {en} } @inproceedings{PagonisHartungWuetal.2024, author = {Pagonis, Panagiotis and Hartung, Kai and Wu, Di and Georges, Munir and Gr{\"o}ttrup, S{\"o}ren}, title = {Analysis of Knowledge Tracing performance on synthesised student data}, publisher = {Universit{\"a}t Bamberg}, address = {Bamberg}, url = {https://sme.uni-bamberg.de/ai4ai/}, year = {2024}, language = {en} } @unpublished{PagonisHartungWuetal.2024, author = {Pagonis, Panagiotis and Hartung, Kai and Wu, Di and Georges, Munir and Gr{\"o}ttrup, S{\"o}ren}, title = {Analysis of Knowledge Tracing performance on synthesised student data}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2401.16832}, year = {2024}, abstract = {Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.}, language = {en} }