Kristian Kersting

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Luc De Raedtprofessor, KU LeuvenEmail verificata su cs.kuleuven.be
Sriraam NatarajanUniversity of Texas at DallasEmail verificata su utdallas.edu
Christian BauckhageProf. of Computer Science, University of Bonn, Fraunhofer IAISEmail verificata su bit.uni-bonn.de
Mirwaes WahabzadaBASF Digital Farming GmbHEmail verificata su basf.com
Tushar KhotAllen Institute for AI; University of Wisconsin-MadisonEmail verificata su cs.wisc.edu
Marion NeumannWashington University in St. LouisEmail verificata su wustl.edu
Alejandro MolinaTU DarmstadtEmail verificata su cs.tu-darmstadt.de
Fabian Hadijigoedle.io GmbHEmail verificata su goedle.io
Wolfram BurgardProfessor of Computer Science, University of FreiburgEmail verificata su informatik.uni-freiburg.de
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinEmail verificata su cs.wisc.edu
Christian ThurauAmbidexterEmail verificata su ambidexter.gmbh
Christian PlagemannGoogle ResearchEmail verificata su google.com
Babak AhmadiFraunhofer IAISEmail verificata su iais.fraunhofer.de
Anne-Katrin MahleinInstitute of Sugar Beet Reserach, GöttingenEmail verificata su ifz-goettingen.de
Martin MladenovGoogle ResearchEmail verificata su google.com
Zhao XuNEC Labs EuropeEmail verificata su acm.org
Petra MutzelProfessorin für Informatik, Universität BonnEmail verificata su cs.uni-bonn.de
Antonio VergariPostDoc, University of California, Los AngelesEmail verificata su cs.ucla.edu
Roman GarnettWashington University in St. LouisEmail verificata su wustl.edu
Saket JoshiApplied Scientist, AmazonEmail verificata su amazon.com

Kristian Kersting

Professor of Artificial Intelligence and Machine Learning, hessian.ai, TU Darmstadt, Germany

Email verificata su cs.tu-darmstadt.de - Home page

Artificial Intelligence Statistical Relational Learning Deep Probabilistic Programming Probabilistic Circuits


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007	1761	2007
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008	479	2008
Bayesian logic programs K Kersting, L De Raedt arXiv preprint cs/0111058, 2001	315	2001
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	310	2007
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling Aaai 8, 1062-1068, 2008	227	2008
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003	218	2003
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007	198	2007
Statistical relational artificial intelligence: Logic, probability, and computation LD Raedt, K Kersting, S Natarajan, D Poole Synthesis Lectures on Artificial Intelligence and Machine Learning 10 (2), 1-189, 2016	187	2016
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012	184	2012
Towards combining inductive logic programming with Bayesian networks K Kersting, L De Raedt International Conference on Inductive Logic Programming, 118-131, 2001	181	2001
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	153	2014
Bellman goes relational K Kersting, MV Otterlo, L De Raedt Proceedings of the twenty-first international conference on Machine learning, 59, 2004	149	2004
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 (1), 25-56, 2012	148	2012
Benchmark data sets for graph kernels, 2016 K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann URL http://graphkernels. cs. tu-dortmund. de 795, 2016	146	2016
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	132	2012
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	123	2006
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine Learning 102 (2), 209-245, 2016	120	2016
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), 1-15, 2015	117	2015
Lifted Probabilistic Inference. K Kersting ECAI, 33-38, 2012	108	2012
nFOIL: Integrating naıve bayes and FOIL N Landwehr, K Kersting, L De Raedt Proceedings of the twentieth national conference on artificial intelligence …, 2005	105	2005

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