| Completely random measures for modelling block-structured sparse networks T Herlau, MN Schmidt, M Mørup Advances in Neural Information Processing Systems 29, 2016 | 56 | 2016 |
| Infinite-degree-corrected stochastic block model T Herlau, MN Schmidt, M Mørup Physical review E 90 (3), 032819, 2014 | 28 | 2014 |
| Modeling temporal evolution and multiscale structure in networks T Herlau, M Mørup, M Schmidt International Conference on Machine Learning, 960-968, 2013 | 21 | 2013 |
| Detecting hierarchical structure in networks T Herlau, M Mørup, MN Schmidt, LK Hansen 2012 3rd International Workshop on Cognitive Information Processing (CIP), 1-6, 2012 | 21 | 2012 |
| Introduction to machine learning and data mining T Herlau, MN Schmidt, M Mørup Lecture notes of the course of the same name given at DTU (Technical …, 2016 | 16 | 2016 |
| Comparing structural brain connectivity by the infinite relational model KS Ambrosen, T Herlau, T Dyrby, MN Schmidt, M Mørup 2013 International Workshop on Pattern Recognition in Neuroimaging, 50-53, 2013 | 16 | 2013 |
| Modelling dense relational data T Herlau, M Mørup, MN Schmidt, LK Hansen 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2012 | 16 | 2012 |
| Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links FK Glückstad, T Herlau, MN Schmidt, M Mørup Artificial intelligence and law 22, 61-108, 2014 | 15* | 2014 |
| Nonparametric Bayesian models of hierarchical structure in complex networks MN Schmidt, T Herlau, M Mørup arXiv preprint arXiv:1311.1033, 2013 | 11 | 2013 |
| Advances in Neural Information Processing Systems T Herlau, MN Schmidt, M Mørup, DD Lee, M Sugiyama, LU Von, I Guyon, ... Curran Associates, Red Hook 29, 4260-4268, 2016 | 10 | 2016 |
| Nonparametric statistical structuring of knowledge systems using binary feature matches M Mørup, FK Glückstad, T Herlau, MN Schmidt 2014 IEEE International Workshop on Machine Learning for Signal Processing …, 2014 | 10 | 2014 |
| Reinforcement learning of causal variables using mediation analysis T Herlau, R Larsen Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6910-6917, 2022 | 6 | 2022 |
| Bayesian dropout T Herlau, MN Schmidt, M Mørup Procedia Computer Science 201, 771-776, 2022 | 6 | 2022 |
| Analysis of conceptualization patterns across groups of people FK Glückstad, T Herlau, MN Schmidt, M Mørup, R Rzepka, K Araki 2013 Conference on Technologies and Applications of Artificial Intelligence …, 2013 | 5 | 2013 |
| Sequential decision making T Herlau Freely available online, 2024 | 4 | 2024 |
| Analysis of subjective conceptualizations towards collective concept modelling FK Glückstad, T Herlau, MN Schmidt, M Mørup 人工知能学会全国大会論文集 第 27 回 (2013), 4C1IOS4b5-4C1IOS4b5, 2013 | 4 | 2013 |
| Causal variables from reinforcement learning using generalized Bellman equations T Herlan arXiv preprint arXiv:2010.15745, 2020 | 3 | 2020 |
| Joint modelling of structural and functional brain networks KW Andersen, T Herlau, M Mørup, MN Schmidt, KH Madsen, M Lyksborg, ... 2nd NIPS Workshop on Machine Learning and Interpretation in NeuroImaging …, 2012 | 3 | 2012 |
| Probability trees and the value of a single intervention T Herlau arXiv preprint arXiv:2205.08779, 2022 | 1 | 2022 |
| Moral reinforcement learning using actual causation T Herlau 2022 2nd International Conference on Computer, Control and Robotics (ICCCR …, 2022 | 1 | 2022 |
