Kristian Kersting
Kristian Kersting
Professor of AI & ML, TU Darmstadt, Co-Director, DFKI, Germany, CLAIRE & ELLIS
Verified email at - Homepage
Cited by
Cited by
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
Probabilistic Inductive Logic Programming
L De Raedt, K Kersting
Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 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
Adaptive Bayesian logic programs
K Kersting, L De Raedt
International Conference on Inductive Logic Programming, 104-117, 2001
Statistical relational artificial intelligence: Logic, probability, and computation
L De Raedt, K Kersting, S Natarajan, D Poole
Springer Nature, 2022
Propagation kernels: efficient graph kernels from propagated information
M Neumann, R Garnett, C Bauckhage, K Kersting
Machine learning 102, 209-245, 2016
DeepDB: Learn from Data, not from Queries!
B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig
PVLDB 13 (7), 2020
Lifted Probabilistic Inference with Counting Formulas.
B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling
AAAI 8, 1062-1068, 2008
Explanatory Interactive Machine Learning
S Teso, K Kersting
Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019
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
Probabilistic logic learning
L De Raedt, K Kersting
ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003
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
Bayesian Logic Programming: Theory and Tool
K Kersting, L De Raedt
Introduction to Statistical Relational Learning, 291, 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
Towards combining inductive logic programming with Bayesian networks
K Kersting, L De Raedt
International Conference on Inductive Logic Programming, 118-131, 2001
Counting belief propagation
K Kersting, B Ahmadi, S Natarajan
arXiv preprint arXiv:1205.2637, 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
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
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
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
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