Kristian Kersting

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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 IAISVerified 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 cs.wisc.edu
Fabian Hadijigoedle.io GmbHVerified email at goedle.io
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenVerified email at ifz-goettingen.de
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinVerified email at cs.wisc.edu
Christian ThurauN26Verified email at n26.com
Babak AhmadiFraunhofer IAISVerified email at iais.fraunhofer.de
Martin MladenovGoogle ResearchVerified email at google.com
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.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
Robert PeharzAssistant Professor of AI, TU GrazVerified email at tugraz.at
Christian PlagemannGoogle ResearchVerified email at google.com
Zhao XuNEC Labs EuropeVerified email at acm.org
Constantin A. RothkopfProfessor, Technical University Darmstadt & Adjunct Fellow, FIASVerified email at cogsci.tu-darmstadt.de

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 C Morris, NM Kriege, F Bause, K Kersting, P Mutzel, M Neumann arXiv preprint arXiv:2007.08663, 2020	543	2020
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008	469	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	431	2007
Adaptive Bayesian logic programs K Kersting, L De Raedt International Conference on Inductive Logic Programming, 104-117, 2001	384	2001
Statistical relational artificial intelligence: Logic, probability, and computation L De Raedt, K Kersting, S Natarajan, D Poole Springer Nature, 2022	334*	2022
Benchmark data sets for graph kernels K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann	271	2016
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine learning 102, 209-245, 2016	245	2016
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling Aaai 8, 1062-1068, 2008	241	2008
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003	226	2003
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007	217	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	209	2014
Towards combining inductive logic programming with Bayesian networks K Kersting, L De Raedt International Conference on Inductive Logic Programming, 118-131, 2001	197	2001
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012	190	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	185	2019
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	184	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	181	2015
Explanatory Interactive Machine Learning S Teso, K Kersting Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019	179	2019
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	173	2012
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	160	2020
Bellman goes relational K Kersting, MV Otterlo, L De Raedt Proceedings of the twenty-first international conference on Machine learning, 59, 2004	160	2004

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