Guy Van den Broeck

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Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityEmail verificata su cs.kuleuven.be
Wannes MeertDTAI, KU LeuvenEmail verificata su cs.kuleuven.be
Yitao LiangPeking UniversityEmail verificata su pku.edu.cn
Antonio VergariReader (Associate Professor), University of Edinburgh, UKEmail verificata su ed.ac.uk
YooJung ChoiAssistant Professor, Arizona State UniversityEmail verificata su asu.edu
Daan FierensDepartment of Computer Science, KULeuvenEmail verificata su cs.kuleuven.be
Angelika KimmigKU LeuvenEmail verificata su cs.kuleuven.be
Jesse DavisProfessor, Department of Computer Science, KU LeuvenEmail verificata su cs.kuleuven.be
Anji LiuPh.D. Student of Computer Science, UCLAEmail verificata su cs.ucla.edu
Dan SuciuUniversity of WashingtonEmail verificata su cs.washington.edu
Arthur ChoiKennesaw State UniversityEmail verificata su kennesaw.edu
Kareem AhmedCS PhD Student at UCLAEmail verificata su cs.ucla.edu
Todd MillsteinProfessor of Computer Science, UCLAEmail verificata su cs.ucla.edu
Tal FriedmanPhD Student, UCLAEmail verificata su cs.ucla.edu
Steven HoltzenNortheastern UniversityEmail verificata su northeastern.edu
Kai-Wei ChangAssociate Professor, UCLAEmail verificata su kwchang.net
Jonas VlasselaerAI Expert at DeltaRayEmail verificata su deltaray.eu
Honghua ZhangUniversity of California, Los AngelesEmail verificata su cs.ucla.edu
Mathias NiepertUniversity of Stuttgart & NEC Labs EuropeEmail verificata su ki.uni-stuttgart.de
Zhe ZengUniversity of California, Los AngelesEmail verificata su cs.ucla.edu

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Guy Van den Broeck

Professor and Samueli Fellow, UCLA

Email verificata su cs.ucla.edu - Home page

Artificial Intelligence Probabilistic Programming Probabilistic Circuits Neurosymbolic AI


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, G Van den Broeck ICML, 2018	557	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	437	2013
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	247	2022
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	239	2011
Probabilistic sentential decision diagrams D Kisa, G Van den Broeck, A Choi, A Darwiche Fourteenth International Conference on the Principles of Knowledge …, 2014	201	2014
Probabilistic inference in hybrid domains by weighted model integration V Belle, A Passerini, G Van den Broeck Proceedings of 24th International Joint Conference on Artificial …, 2015	132	2015
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	126*	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	121	2013
Probabilistic circuits: A unifying framework for tractable probabilistic models Y Choi, A Vergari, G Van den Broeck UCLA. URL: http://starai. cs. ucla. edu/papers/ProbCirc20. pdf, 6, 2020	113	2020
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	112	2017
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	111	2020
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	107	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	106	2009
On the paradox of learning to reason from data H Zhang, LH Li, T Meng, KW Chang, G Van den Broeck IJCAI, 2022	103	2022
Algebraic model counting A Kimmig, G Van den Broeck, L De Raedt Journal of Applied Logic 22, 46-62, 2017	103*	2017
Inducing probabilistic relational rules from probabilistic examples L De Raedt, A Dries, I Thon, G Van den Broeck, M Verbeke Twenty-fourth international joint conference on artificial intelligence, 2015	100	2015
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference M Niepert, G Van den Broeck AAAI Conference on Artificial Intelligence, 2014	94	2014
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	89	2010
An algebraic Prolog for reasoning about possible worlds A Kimmig, G Van den Broeck, L De Raedt AAAI Conference on Artificial Intelligence., 2011	88	2011
Lifted Inference and Learning in Statistical Relational Models G Van den Broeck KU Leuven, 2013	85	2013

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