| 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 | 373 | 2013 |
| A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, G Van den Broeck ICML, 2018 | 357 | 2018 |
| 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 | 212 | 2011 |
| Probabilistic sentential decision diagrams D Kisa, G Van den Broeck, A Choi, A Darwiche Fourteenth International Conference on the Principles of Knowledge …, 2014 | 156 | 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 | 114 | 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 | 99* | 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 | 97 | 2013 |
| 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 | 95 | 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 | 94 | 2009 |
| 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 | 92 | 2017 |
| On the Tractability of SHAP Explanations G Van den Broeck, A Lykov, M Schleich, D Suciu AAAI 2021, 2020 | 84 | 2020 |
| Inducing probabilistic relational rules from probabilistic examples L De Raedt, A Dries, I Thon, G Van den Broeck, M Verbeke Proceedings of 24th international joint conference on artificial …, 2015 | 81 | 2015 |
| Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference M Niepert, G Van den Broeck AAAI Conference on Artificial Intelligence, 2014 | 77 | 2014 |
| An algebraic Prolog for reasoning about possible worlds A Kimmig, G Van den Broeck, L De Raedt AAAI Conference on Artificial Intelligence., 2011 | 77 | 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 | 76 | 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 | 75 | 2010 |
| Lifted Inference and Learning in Statistical Relational Models G Van den Broeck KU Leuven, 2013 | 72 | 2013 |
| Algebraic model counting A Kimmig, G Van den Broeck, L De Raedt Journal of Applied Logic 22, 46-62, 2017 | 68* | 2017 |
| Open-World Probabilistic Databases. II Ceylan, A Darwiche, G Van den Broeck 15th International Conference on Principles of Knowledge Representation and …, 2016 | 66* | 2016 |
| Tractable learning for structured probability spaces: A case study in learning preference distributions A Choi, G Van den Broeck, A Darwiche Proceedings of 24th International Joint Conference on Artificial …, 2015 | 65 | 2015 |
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
