Guy Van den Broeck

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Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
Wannes MeertDTAI, KU LeuvenVerified email at cs.kuleuven.be
Yitao LiangPeking UniversityVerified email at pku.edu.cn
YooJung ChoiAssistant Professor, Arizona State UniversityVerified email at asu.edu
Angelika KimmigKU LeuvenVerified email at cs.kuleuven.be
Antonio VergariReader (Associate Professor), University of Edinburgh, UKVerified email at ed.ac.uk
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
Daan FierensDepartment of Computer Science, KULeuvenVerified email at cs.kuleuven.be
Arthur ChoiKennesaw State University, formerly UCLAVerified email at kennesaw.edu
Dan SuciuUniversity of WashingtonVerified email at cs.washington.edu
Tal FriedmanPhD Student, UCLAVerified email at cs.ucla.edu
Todd MillsteinProfessor of Computer Science, UCLAVerified email at cs.ucla.edu
Steven HoltzenNortheastern UniversityVerified email at northeastern.edu
Jonas VlasselaerAI Expert at DeltaRayVerified email at deltaray.eu
Anji LiuPh.D. Student of Computer Science, UCLAVerified email at cs.ucla.edu
Pasha KhosraviComputer Science PhD Student at UCLAVerified email at cs.ucla.edu
Nima TaghipourPostdoctoral Researcher, KU LeuvenVerified email at cs.kuleuven.be
Andrea PasseriniAssociate Professor, University of TrentoVerified email at disi.unitn.it
Mathias NiepertUniversity of Stuttgart & NEC Labs EuropeVerified email at simtech.uni-stuttgart.de
David PooleProfessor of Computer Science, University of British ColumbiaVerified email at cs.ubc.ca

Guy Van den Broeck

Associate Professor and Samueli Fellow, UCLA

Verified email at cs.ucla.edu - Homepage

Artificial Intelligence Probabilistic Programming Probabilistic Circuits Neurosymbolic AI


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A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, G Van den Broeck ICML, 2018	400	2018
Inference and learning in probabilistic logic programs using weighted Boolean formulas D Fierens, G Van den Broeck, J Renkens, D Shterionov, B Gutmann, ... Theory and Practice of Logic Programming, 2013	390	2013
Lifted probabilistic inference by first-order knowledge compilation G Van den Broeck, N Taghipour, W Meert, J Davis, L De Raedt International Joint Conference on Artificial Intelligence (IJCAI), 2011	220	2011
Probabilistic sentential decision diagrams D Kisa, G Van den Broeck, A Choi, A Darwiche Fourteenth International Conference on the Principles of Knowledge …, 2014	171	2014
Probabilistic inference in hybrid domains by weighted model integration V Belle, A Passerini, G Van den Broeck, Q Yang, M Wooldridge Proceedings of 24th International Joint Conference on Artificial …, 2015	117	2015
On the tractability of SHAP explanations G Van den Broeck, A Lykov, M Schleich, D Suciu Journal of Artificial Intelligence Research 74, 851-886, 2022	112	2022
On the completeness of first-order knowledge compilation for lifted probabilistic inference G Van den Broeck Annual Conference on Neural Information Processing Systems (NeurIPS), 2011	107*	2011
Skolemization for Weighted First-Order Model Counting G Van den Broeck, W Meert, A Darwiche Proceedings of the 14th International Conference on Principles of Knowledge …, 2013	104	2013
Learning the structure of probabilistic sentential decision diagrams Y Liang, J Bekker, G Van den Broeck Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017	99	2017
Inference in probabilistic logic programs using weighted CNF's D Fierens, G Van den Broeck, I Thon, B Gutmann, L De Raedt Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence …, 2011	98	2011
Monte-Carlo tree search in poker using expected reward distributions G Van den Broeck, K Driessens, J Ramon Advances in Machine Learning, 367-381, 2009	96	2009
Inducing probabilistic relational rules from probabilistic examples L De Raedt, A Dries, I Thon, G Van den Broeck, M Verbeke, Q Yang, ... Proceedings of 24th international joint conference on artificial …, 2015	85	2015
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference M Niepert, G Van den Broeck AAAI Conference on Artificial Intelligence, 2014	79	2014
An algebraic Prolog for reasoning about possible worlds A Kimmig, G Van den Broeck, L De Raedt AAAI Conference on Artificial Intelligence., 2011	79	2011
On the Complexity and Approximation of Binary Evidence in Lifted Inference G Van den Broeck, A Darwiche Advances in Neural Information Processing Systems 26 (NIPS), 2013	78	2013
DTProbLog: A decision-theoretic probabilistic Prolog G Van den Broeck, I Thon, M van Otterlo, L De Raedt AAAI conference on artificial intelligence (AAAI 2010), 2010	78	2010
Algebraic model counting A Kimmig, G Van den Broeck, L De Raedt Journal of Applied Logic 22, 46-62, 2017	76*	2017
Lifted Inference and Learning in Statistical Relational Models G Van den Broeck KU Leuven, 2013	75	2013
Scaling exact inference for discrete probabilistic programs S Holtzen, G Van den Broeck, T Millstein Proceedings of the ACM on Programming Languages 4 (OOPSLA), 1-31, 2020	69*	2020
Open-World Probabilistic Databases. II Ceylan, A Darwiche, G Van den Broeck 15th International Conference on Principles of Knowledge Representation and …, 2016	66*	2016

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