| A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, G Van den Broeck ICML, 2018 | 534 | 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 | 430 | 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 | 237 | 2011 |
| 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 | 202 | 2022 |
| Probabilistic sentential decision diagrams D Kisa, G Van den Broeck, A Choi, A Darwiche Fourteenth International Conference on the Principles of Knowledge …, 2014 | 198 | 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 | 129 | 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 | 124* | 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 | 119 | 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 | 110 | 2017 |
| 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 | 106 | 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 | 106 | 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 | 103 | 2009 |
| 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 | 102* | 2020 |
| 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 | 97 | 2015 |
| Algebraic model counting A Kimmig, G Van den Broeck, L De Raedt Journal of Applied Logic 22, 46-62, 2017 | 95* | 2017 |
| On the paradox of learning to reason from data H Zhang, LH Li, T Meng, KW Chang, GV Broeck arXiv preprint arXiv:2205.11502, 2022 | 91 | 2022 |
| Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference M Niepert, G Van den Broeck AAAI Conference on Artificial Intelligence, 2014 | 90 | 2014 |
| 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 |
| 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 | 87 | 2010 |
| Lifted Inference and Learning in Statistical Relational Models G Van den Broeck KU Leuven, 2013 | 83 | 2013 |
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
