Ising models for binary clustering via adiabatic quantum computing C Bauckhage, E Brito, K Cvejoski, C Ojeda, R Sifa, S Wrobel Energy Minimization Methods in Computer Vision and Pattern Recognition: 11th …, 2018 | 35 | 2018 |

Predicting retention in sandbox games with tensor factorization-based representation learning R Sifa, S Srikanth, A Drachen, C Ojeda, C Bauckhage 2016 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2016 | 35 | 2016 |

Adiabatic Quantum Computing for Kernel k= 2 Means Clustering. C Bauckhage, C Ojeda, R Sifa, S Wrobel LWDA, 21-32, 2018 | 24 | 2018 |

User churn migration analysis with DEDICOM R Sifa, C Ojeda, C Bauckhage Proceedings of the 9th ACM Conference on Recommender Systems, 321-324, 2015 | 17 | 2015 |

Adiabatic quantum computing for binary clustering C Bauckhage, E Brito, K Cvejoski, C Ojeda, R Sifa, S Wrobel arXiv preprint arXiv:1706.05528, 2017 | 11 | 2017 |

Informed machine learning through functional composition. C Bauckhage, C Ojeda, J Schücker, R Sifa, S Wrobel LWDA, 33-37, 2018 | 9 | 2018 |

Comparison between different oxygen adsorption mechanisms for the catalytic oxidation of CO on a surface C Ojeda, GM Buendía Journal of Computational Methods in Sciences and Engineering 12 (4-6), 261-267, 2012 | 8 | 2012 |

Learning deep generative models for queuing systems C Ojeda, K Cvejoski, B Georgiev, C Bauckhage, J Schuecker, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 9214-9222, 2021 | 7 | 2021 |

Towards shortest paths via adiabatic quantum computing C Bauckhage, E Brito, K Cvejoski, C Ojeda, J Schücker, R Sifa Proc. Mining Learn. Graphs, 2018 | 7 | 2018 |

Variational Bayesian inference for nonlinear Hawkes process with Gaussian process self-effects N Malem-Shinitski, C Ojeda, M Opper Entropy 24 (3), 356, 2022 | 6 | 2022 |

A score-based approach for training schrödinger bridges for data modelling L Winkler, C Ojeda, M Opper Entropy 25 (2), 316, 2023 | 5 | 2023 |

Stochastic Control for Bayesian Neural Network Training L Winkler, C Ojeda, M Opper Entropy 24 (8), 1097, 2022 | 5 | 2022 |

Dynamic Review-based Recommenders K Cvejoski, RJ Sánchez, C Bauckhage, C Ojeda Data Science–Analytics and Applications: Proceedings of the 4th …, 2022 | 5 | 2022 |

Towards German word embeddings: A use case with predictive sentiment analysis E Brito, R Sifa, K Cvejoski, C Ojeda, C Bauckhage Data Science–Analytics and Applications: Proceedings of the 1st …, 2017 | 5 | 2017 |

Circadian cycles and work under pressure: A stochastic process model for e-learning population dynamics C Backhage, C Ojeda, R Sifa Data Science–Analytics and Applications: Proceedings of the 1st …, 2017 | 5 | 2017 |

Neural dynamic focused topic model K Cvejoski, RJ Sánchez, C Ojeda Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 12719 …, 2023 | 4 | 2023 |

Recurrent point processes for dynamic review models K Cvejoski, RJ Sanchez, B Georgiev, J Schuecker, C Bauckhage, ... arXiv preprint arXiv:1912.04132, 2019 | 4 | 2019 |

Inverse dynamical inheritance in stack exchange taxonomies C Ojeda, K Cvejoski, R Sifa, C Bauckhage Proceedings of the International AAAI Conference on Web and Social Media 11 …, 2017 | 4 | 2017 |

The future is different: Large pre-trained language models fail in prediction tasks K Cvejoski, RJ Sánchez, C Ojeda arXiv preprint arXiv:2211.00384, 2022 | 3 | 2022 |

An irregularly spaced first-order moving average model C Ojeda, W Palma, S Eyheramendy, F Elorrieta arXiv preprint arXiv:2105.06395, 2021 | 3 | 2021 |