TUDataset: A collection of benchmark datasets for learning with graphs MN Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting ... ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020), 2020 | 996* | 2020 |
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008 | 482 | 2008 |
Most likely heteroscedastic Gaussian process regression K Kersting, C Plagemann, P Pfaff, W Burgard Proceedings of the 24th international conference on Machine learning, 393-400, 2007 | 449 | 2007 |
Adaptive Bayesian logic programs K Kersting, L De Raedt International Conference on Inductive Logic Programming, 104-117, 2001 | 382 | 2001 |
Statistical relational artificial intelligence: Logic, probability, and computation L De Raedt, K Kersting, S Natarajan, D Poole Springer Nature, 2022 | 367* | 2022 |
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine learning 102, 209-245, 2016 | 273 | 2016 |
DeepDB: Learn from Data, not from Queries! B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig PVLDB 13 (7), 2020 | 248 | 2020 |
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling AAAI 8, 1062-1068, 2008 | 240 | 2008 |
Predicting player churn in the wild F Hadiji, R Sifa, A Drachen, C Thurau, K Kersting, C Bauckhage 2014 ieee conference on computational intelligence and games, 1-8, 2014 | 223 | 2014 |
Explanatory Interactive Machine Learning S Teso, K Kersting Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019 | 222 | 2019 |
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003 | 222 | 2003 |
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007 | 220 | 2007 |
Making deep neural networks right for the right scientific reasons by interacting with their explanations P Schramowski, W Stammer, S Teso, A Brugger, F Herbert, X Shao, ... Nature Machine Intelligence 2 (8), 476-486, 2020 | 209 | 2020 |
Large pre-trained language models contain human-like biases of what is right and wrong to do P Schramowski, C Turan, N Andersen, CA Rothkopf, K Kersting Nature Machine Intelligence 4 (3), 258-268, 2022 | 204 | 2022 |
Towards combining inductive logic programming with Bayesian networks K Kersting, L De Raedt International Conference on Inductive Logic Programming, 118-131, 2001 | 200 | 2001 |
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012 | 195 | 2012 |
Gradient-based boosting for statistical relational learning: The relational dependency network case S Natarajan, T Khot, K Kersting, B Gutmann, J Shavlik Machine Learning 86, 25-56, 2012 | 190 | 2012 |
Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions M Kuska, M Wahabzada, M Leucker, HW Dehne, K Kersting, EC Oerke, ... Plant methods 11, 1-15, 2015 | 188 | 2015 |
Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis C Römer, M Wahabzada, A Ballvora, F Pinto, M Rossini, C Panigada, ... Functional Plant Biology 39 (11), 878-890, 2012 | 181 | 2012 |
Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007 | 176 | 2007 |