Kristian Kersting
Kristian Kersting
Professor of AI & ML, TU Darmstadt, Co-Director, DFKI, Germany, CLAIRE & ELLIS
Verified email at - Homepage
Cited by
Cited by
Probabilistic Inductive Logic Programming
L De Raedt, K Kersting
Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008
Tudataset: A collection of benchmark datasets for learning with graphs
C Morris, NM Kriege, F Bause, K Kersting, P Mutzel, M Neumann
arXiv preprint arXiv:2007.08663, 2020
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
Benchmark data sets for graph kernels
K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann
Lifted Probabilistic Inference with Counting Formulas.
B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling
Aaai 8, 1062-1068, 2008
Propagation kernels: efficient graph kernels from propagated information
M Neumann, R Garnett, C Bauckhage, K Kersting
Machine learning 102, 209-245, 2016
Probabilistic logic learning
L De Raedt, K Kersting
ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003
Bayesian Logic Programming: Theory and Tool
K Kersting, L De Raedt
Introduction to Statistical Relational Learning, 291, 2007
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
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
Deepdb: Learn from data, not from queries!
B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig
arXiv preprint arXiv:1909.00607, 2019
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
Explanatory Interactive Machine Learning
S Teso, K Kersting
Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019
Bellman goes relational
K Kersting, MV Otterlo, L De Raedt
Proceedings of the twenty-first international conference on Machine learning, 59, 2004
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
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