Kristian Kersting

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Citations	18265	11285
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Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
Christian BauckhageProf. of Computer Science, University of Bonn, Fraunhofer IAIS, LAMARR InstituteVerified email at bit.uni-bonn.de
Alejandro MolinaAmazonVerified email at amazon.com
Mirwaes WahabzadaBASF Digital Farming GmbHVerified email at basf.com
Marion NeumannWashington University in St. LouisVerified email at wustl.edu
Tushar KhotAllen Institute for AI; University of Wisconsin-MadisonVerified email at allenai.org
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenVerified email at ifz-goettingen.de
Fabian Hadijigoedle.io GmbHVerified email at goedle.io
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinVerified email at cs.wisc.edu
Christian ThurauN26Verified email at n26.com
Martin MladenovGoogle ResearchVerified email at google.com
Babak AhmadiFraunhofer IAISVerified email at iais.fraunhofer.de
Constantin A. RothkopfProfessor, Technical University Darmstadt & Adjunct Fellow, FIASVerified email at cogsci.tu-darmstadt.de
Petra MutzelProfessorin für Informatik, Universität BonnVerified email at cs.uni-bonn.de
Antonio VergariReader (Associate Professor), University of Edinburgh, UKVerified email at ed.ac.uk
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.de
Robert PeharzAssistant Professor of AI, TU GrazVerified email at tugraz.at
Christopher MorrisRWTH Aachen UniversityVerified email at cs.rwth-aachen.de
Christian PlagemannGoogle ResearchVerified email at google.com

Kristian Kersting

Professor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLIS

Verified email at cs.tu-darmstadt.de - Homepage

Artificial Intelligence Neurosymbolic AI Probabilistic Circuits Interpretability Machine Learning


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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	1164*	2020
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008	492	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	466	2007
Statistical relational artificial intelligence: Logic, probability, and computation L De Raedt, K Kersting, S Natarajan, D Poole Springer Nature, 2022	388*	2022
Adaptive Bayesian logic programs K Kersting, L De Raedt International Conference on Inductive Logic Programming, 104-117, 2001	383	2001
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine learning 102, 209-245, 2016	294	2016
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	284	2022
DeepDB: Learn from Data, not from Queries! B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig PVLDB 13 (7), 2020	284	2020
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling AAAI 8, 1062-1068, 2008	248	2008
Explanatory Interactive Machine Learning S Teso, K Kersting Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019	246	2019
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	240	2020
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	234	2014
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003	224	2003
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007	223	2007
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	206	2015
Towards combining inductive logic programming with Bayesian networks K Kersting, L De Raedt International Conference on Inductive Logic Programming, 118-131, 2001	204	2001
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012	198	2012
Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models P Schramowski, M Brack, B Deiseroth, K Kersting Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023	193	2023
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	192	2012
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	185	2012

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