@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} } @article{HebestreitGroettrupEmdenetal.2012, author = {Hebestreit, Katja and Gr{\"o}ttrup, S{\"o}ren and Emden, Daniel and Veerkamp, Jannis and Ruckert, Christian and Klein, Hans-Ulrich and M{\"u}ller-Tidow, Carsten and Dugas, Martin}, title = {Leukemia Gene Atlas - A Public Platform for Integrative Exploration of Genome-Wide Molecular Data}, volume = {7}, pages = {e39148}, journal = {PLOS ONE}, number = {6}, publisher = {PLOS}, address = {San Francisco}, issn = {1932-6203}, doi = {https://doi.org/10.1371/journal.pone.0039148}, year = {2012}, abstract = {Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org.}, 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{HartungJaegerGroettrupetal.2022, author = {Hartung, Kai and J{\"a}ger, Gerhard and Gr{\"o}ttrup, S{\"o}ren and Georges, Munir}, title = {Typological Word Order Correlations with Logistic Brownian Motion}, pages = {2022.sigtyp-1.3}, booktitle = {Proceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022)}, editor = {Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg}, isbn = {978-1-955917-93-3}, url = {https://aclanthology.org/2022.sigtyp-1.3}, pages = {22 -- 26}, year = {2022}, abstract = {In this study we address the question to what extent syntactic word-order traits of different languages have evolved under correlation and whether such dependencies can be found universally across all languages or restricted to specific language families.To do so, we use logistic Brownian Motion under a Bayesian framework to model the trait evolution for 768 languages from 34 language families. We test for trait correlations both in single families and universally over all families. Separate models reveal no universal correlation patterns and Bayes Factor analysis of models over all covered families also strongly indicate lineage specific correlation patters instead of universal dependencies.}, 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} } @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} } @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} }