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Luc De Raedt

Position: Visiting Professor School/office: School of Science and Technology

Email: bHVjLmRlLXJhZWR0O29ydS5zZQ==

Phone: No number available

Room: T2243

Research subject

Computer Science

Research environments

Centre for Applied Autonomous Sensor Systems (AASS)

About Luc De Raedt

I am very excited to be a Wallenberg Guest Professor in Computer Science and Artificial Intelligence at Örebro University. Thanks to the generous support of the WASP program I will be building a group that focuses on machine learning and machine reasoning within AASS. The integration of learning and reasoning in artificial intelligence is one of the key open questions in AI today. Our group will also apply these techniques in autonomous systems and sensors.

I am also a full professor at KU Leuven (Belgium) and the director of the KU Leuven AI Institute. I am an ERC AdG Grant holder, a EurAI and AAAI Fellow, and an IJCAI Trustee. My full CV is available via the URL https://wms.cs.kuleuven.be/people/lucderaedt

Research groups
Publications

Research groups

Machine Perception and Interaction Lab

Publications

Articles in journals

Zuidberg dos Martires, P. , De Raedt, L. & Kimmig, A. (2024). Declarative probabilistic logic programming in discrete-continuous domains. Artificial Intelligence, 337. [BibTeX]
Marra, G. , Dumancic, S. , Manhaeve, R. & De Raedt, L. (2024). From statistical relational to neurosymbolic artificial intelligence: A survey. Artificial Intelligence, 328. [BibTeX]
Verreet, V. , De Raedt, L. & Bekker, J. (2024). Modeling PU learning using probabilistic logic programming. Machine Learning, 113 (3), 1351-1372. [BibTeX]
Derkinderen, V. , Manhaeve, R. , Zuidberg dos Martires, P. & De Raedt, L. (2024). Semirings for probabilistic and neuro-symbolic logic programming. International Journal of Approximate Reasoning, 171. [BibTeX]
Van Roy, M. , Robberechts, P. , Yang, W. , De Raedt, L. & Davis, J. (2023). A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer. The journal of artificial intelligence research, 77, 517-562. [BibTeX]
Yang, W. , Raskin, J. & De Raedt, L. (2022). Lifted model checking for relational MDPs. Machine Learning (111), 3797-3838. [BibTeX]
Dechter, R. & De Raedt, L. (2022). Preface. IJCAI International Joint Conference on Artificial Intelligence, lvi-lvii. [BibTeX]
Dechter, R. , De Raedt, L. , Sierra, C. , Ortiz, M. & Woltran, S. (2022). WELCOME TO IJCAI-ECAI 2022. IJCAI International Joint Conference on Artificial Intelligence. [BibTeX]
Manhaeve, R. , Dumančić, S. , Kimmig, A. , Demeester, T. & De Raedt, L. (2021). Neural probabilistic logic programming in DeepProbLog. Artificial Intelligence, 298. [BibTeX]
Kolb, S. , Teso, S. , Dries, A. & De Raedt, L. (2020). Predictive spreadsheet autocompletion with constraints. Machine Learning, 109 (2), 307-325. [BibTeX]
Belle, V. & De Raedt, L. (2020). Semiring programming: A semantic framework for generalized sum product problems. International Journal of Approximate Reasoning, 126, 181-201. [BibTeX]
Zuidberg Dos Martires, P. , Kumar, N. , Persson, A. , Loutfi, A. & De Raedt, L. (2020). Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring. Frontiers in Robotics and AI, 7. [BibTeX]
Groß, A. , Kracher, B. , Kraus, J. M. , Kühlwein, S. D. , Pfister, A. S. , Wiese, S. , Luckert, K. , Pötz, O. & et al. (2019). Representing Dynamic Biological Networks With Multi-Scale Probabilistic Models. Communications Biology, 2. [BibTeX]
Antanas, L. , Moreno, P. , Neumann, M. , Pimentel de Figueiredo, R. , Kersting, K. , Santos-Victor, J. & De Raedt, L. (2019). Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. Autonomous Robots, 43 (6), 1393-1418. [BibTeX]
Moldovan, B. , Moreno, P. , Nitti, D. , Santos-Victor, J. & De Raedt, L. (2018). Relational affordances for multiple-object manipulation. Autonomous Robots, 42 (1), 19-44. [BibTeX]
Kimmig, A. , Van den Broeck, G. & De Raedt, L. (2017). Algebraic Model Counting. Journal of Applied Logic, 22, 46-62. [BibTeX]
Oramas, J. , De Raedt, L. & Tuytelaars, T. (2017). Context-based Object Viewpoint Estimation: A 2D Relational Approach. Computer Vision and Image Understanding, 160, 100-113. [BibTeX]
De Raedt, L. , Bui, M. , Deville, Y. & Dieu-Linh, T. (2017). Editors' Introduction to the Special Issue on ‟Information and Communication Technology”. Informatica - journal of computing and informatics, 41 (2), 131-131. [BibTeX]
Dzyuba, V. , van Leeuwen, M. & De Raedt, L. (2017). Flexible constrained sampling with guarantees for pattern mining. Data mining and knowledge discovery, 31, 1266-1293. [BibTeX]
Orsini, F. , Frasconi, P. & De Raedt, L. (2017). kProbLog: an algebraic Prolog for machine learning. Machine Learning (106), 1933-1969. [BibTeX]
Kolb, S. , Paramonov, S. , Guns, T. & De Raedt, L. (2017). Learning constraints in spreadsheets and tabular data. Machine Learning (106), 1441-1468. [BibTeX]
Guns, T. , Dries, A. , Nijssen, S. , Tack, G. & De Raedt, L. (2017). MiningZinc: A declarative framework for constraint-based mining. Artificial Intelligence, 244, 6-29. [BibTeX]
Nitti, D. , Belle, V. , Laet, T. & De Raedt, L. (2017). Planning in hybrid relational MDPs. Machine Learning, 106 (12), 1905-1932. [BibTeX]
Paramonov, S. , van Leeuwen, M. & De Raedt, L. (2017). Relational data factorization. Machine Learning, 106 (12), 1867-1904. [BibTeX]
Le Van, T. , Nijssen, S. , van Leeuwen, M. & De Raedt, L. (2017). Semiring Rank Matrix Factorization. IEEE Transactions on Knowledge and Data Engineering, 29 (8), 1737-1750. [BibTeX]
Bessiere, C. , De Raedt, L. , Guns, T. , Kotthoff, L. , Nanni, M. , Nijssen, S. , O’Sullivan, B. , Paparrizou, A. & et al. (2017). The Inductive Constraint Programming Loop. IEEE Intelligent Systems, 32 (5), 44-52. [BibTeX]
De Raedt, L. , Deville, Y. , Bui, M. & Truong, D. (2016). Editors' Introduction to the Special Issue on: "The Sixth International Symposium on Information and Communication Technology - SoICT 2015". Informatica, 40 (2), 157-157. [BibTeX]
Vlasselaer, J. , Meert, W. , Van den Broeck, G. & De Raedt, L. (2016). Exploiting local and repeated structure in Dynamic Bayesian Networks. Artificial Intelligence, 232, 43-53. [BibTeX]
De Maeyer, D. , Weytjens, B. , De Raedt, L. & Marchal, K. (2016). Network-Based Analysis of eQTL Data to Prioritize Driver Mutations. Genome Biology and Evolution, 23;8 (3), 481-494. [BibTeX]
Nitti, D. , De Laet, T. & De Raedt, L. (2016). Probabilistic logic programming for hybrid relational domains. Machine Learning, 103 (3), 407-449. [BibTeX]
Le Van, T. , van Leeuwen, M. , Fierro, A. C. , De Maeyer, D. , Van den Eynden, J. , Verbeke, L. , De Raedt, L. , Marchal, K. & et al. (2016). Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics, 32 (17), 445-454. [BibTeX]
Vlasselaer, J. , Van den Broeck, G. , Kimmig, A. , Meert, W. & De Raedt, L. (2016). Tp-compilation for inference inprobabilistic logic programs. International Journal of Approximate Reasoning, 78, 15-32. [BibTeX]
Fierens, D. , Vam Den Broeck, G. , Renkens, J. , Shterionov, D. , Gutmann, B. , Thon, I. , Janssens, G. & De Raedt, L. (2015). Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming, 15 (3), 358-401. [BibTeX]
Cussens, J. , De Raedt, L. , Kimmig, A. & Sato, T. (2015). Introduction to the special issue on probability, logic and learning. Theory and Practice of Logic Programming, 15 (2), 145-146. [BibTeX]
De Maeyer, D. , Weytjens, B. , Renkens, J. , De Raedt, L. & Marchal, K. (2015). PheNetic: network-based interpretation of molecular profiling data. Nucleic Acids Research, 43 (W1), 244-250. [BibTeX]
De Raedt, L. & Kimmig, A. (2015). Probabilistic (logic) programming concepts. Machine Learning, 100 (1), 5-47. [BibTeX]
Dzyuba, V. , van Leeuwen, M. , Nijssen, S. & De Raedt, L. (2014). Interactive Learning of Pattern Rankings. International journal on artificial intelligence tools, 23 (6). [BibTeX]
Fox, M. & De Raedt, L. (2014). Introduction to the Special Issue on the ECAI 2012 Turing and Anniversary Track. AI Communications, 27 (1), 1-1. [BibTeX]
Frasconi, P. , Costa, F. , De Raedt, L. & De Grave, K. (2014). kLog: A language for logical and relational learning with kernels. Artificial Intelligence, 217, 117-143. [BibTeX]
Antanas, L. , Van Otterlo, M. , Oramas Mogrovejo, J. , Tuytelaars, T. & De Raedt, L. (2014). There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding. Neurocomputing, 123, 75-85. [BibTeX]
Guns, T. , Nijssen, S. & De Raedt, L. (2013). k-Pattern Set Mining under Constraints. IEEE Transactions on Knowledge and Data Engineering, 25 (2), 402-418. [BibTeX]
Garriga, G. C. , Khardon, R. & De Raedt, L. (2013). Mining closed patterns in relational, graph and network data. Annals of Mathematics and Artificial Intelligence, 69 (4), 315-342. [BibTeX]
De Maeyer, D. , Renkens, J. , Cloots, L. , De Raedt, L. & Marchal, K. (2013). PheNetic: Network-based interpretation of unstructured gene lists in E. coli. Molecular Biosystems, 9 (7), 1594-1603. [BibTeX]

Articles, reviews/surveys

De Bie, T. , De Raedt, L. , Hernandez-Orallo, J. , Hoos, H. H. , Smyth, P. & Williams, C. K. I. (2022). Automating Data Science. Communications of the ACM, 65 (3), 76-87. [BibTeX]

Books

De Raedt, L. , Kersting, K. , Natarajan, S. & Poole, D. (2016). Statistical Relational Artificial Intelligence: Logic, Probability, and Computation.. Morgan & Claypool Publishers. [BibTeX]

Chapters in books

Manhaeve, R. , Marra, G. , Demeester, T. , Dumancic, S. , Kimmig, A. & De Raedt, L. (2022). Neuro-Symbolic AI = Neural + Logical + Probabilistic AI. In: Pascal Hitzler; Md Kamruzzaman Sarker, Neuro-Symbolic Artificial Intelligence: The State of the Art (pp. 173-191). . IOS Press. [BibTeX]
De Raedt, L. (2017). Inductive Logic Programming. In: Claude Sammut, Geoffrey I. Webb, Encyclopedia of machine learning and data mining. New York: Springer-Verlag New York. [BibTeX]
De Raedt, L. (2017). Logic of generality. In: Claude Sammut, Geoffrey I. Webb, Encyclopedia of Machine Learning and Data Mining (pp. 772-780). New York: Springer-Verlag New York. [BibTeX]
De Raedt, L. (2017). Multi-relational Data Mining. In: Claude Sammut, Geoffrey I. Webb, Encyclopedia of machine learning and data mining (pp. 892-893). New York: Springer-Verlag New York. [BibTeX]
De Raedt, L. & Kersting, K. (2017). Statistical relational learning. In: Claude Sammut, Geoffrey I. Webb, Encyclopedia of Machine Learning and Data Mining (pp. 772-780). New York: Springer-Verlag New York. [BibTeX]
De Raedt, L. , Deville, Y. , Bui, M. , Linh, T. T. D. , Thang, H. Q. & Hung, N. M. (2016). Foreword. In: Hoang Minh Son, Proceedings of the Seventh Symposium on Information and Communication Technology (pp. vi-vi). New York: Association for Computing Machinery. [BibTeX]
De Raedt, L. , Dries, A. , Guns, T. & Bessiere, C. (2016). Learning Constraint Satisfaction Problems: an ILP Perspective. In: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach (pp. 96-112). Cham: Springer International Publishing. [BibTeX]
Dries, A. , Guns, T. , Nijssen, S. , Babaki, B. , Le Van, T. , Negrevergne, B. , Paramonov, S. & De Raedt, L. (2016). Modeling in MiningZinc. In: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach (pp. 257-281). Cham: Springer International Publishing. [BibTeX]
Bessiere, C. , De Raedt, L. , Guns, T. , Kotthoff, L. , Nanni, M. , Nijssen, S. , O’Sullivan, B. , Paparrizou, A. & et al. (2016). The Inductive Constraint Programming Loop. In: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach (pp. 303-309). Cham: Springer International Publishing. [BibTeX]
De Raedt, L. , Deville, Y. , Bui, M. , Truong, D. L. , Quyet, T. H. & Le, A. P. (2015). Foreword. In: Huynh Quyet Thang, Phuong Le Anh, Luc De Raedt et al., Proceedings of the Sixth International Symposium on Information and Communication Technology (pp. v-v). New York: Association for Computing Machinery (ACM). [BibTeX]
Moldovan, B. , van Otterlo, M. , De Raedt, L. , Moreno, P. & Santos-Victor, J. (2014). Statistical Relational Learning of Object Affordances for Robotic Manipulation. In: Latest Advances in Inductive Logic Programming (pp. 95-103). London: Imperial College Press. [BibTeX]

Collections (editor)

Bessiere, C. (ed.) , De Raedt, L. (ed.) , Kotthoff, L. (ed.) , Nijssen, S. (ed.) , O'Sullivan, B. (ed.) & Pedreschi, D. (ed.) (2016). Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach. Cham: Springer International Publishing (Lecture Notes in Computer Science 10101). [BibTeX]

Conference papers

Zuidberg dos Martires, P. , Derkinderen, V. , De Raedt, L. & Krantz, M. (2024). Automated Reasoning in Systems Biology: A Necessity for Precision Medicine. In: Pierre Marquis; Magdalena Ortiz; Maurice Pagnucco, Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning. Paper presented at 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), Hanoi, Vietnam, November 2-8, 2024. (pp. 974-980). AAAI Press. [BibTeX]
Venturato, G. , Derkinderen, V. , Zuidberg dos Martires, P. & De Raedt, L. (2024). Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation. In: Michael Wooldridge; Jennifer Dy; Sriraam Natarajan, Proceedings of the 38th AAAI Conference on Artificial Intelligence. Paper presented at 38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, Canada, February 20-27, 2024. (pp. 20567-20576). AAAI Press. [BibTeX]
Hazra, R. , Zuidberg dos Martires, P. & De Raedt, L. (2024). SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge. In: Michael Wooldridge; Jennifer Dy; Sriraam Natarajan, Proceedings of the 38th AAAI Conference on Artificial Intelligence. Paper presented at 38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, Canada, February 20-27, 2024. (pp. 20123-20133). AAAI Press. [BibTeX]
Jiao, Y. , De Raedt, L. & Marra, G. (2024). Valid Text-to-SQL Generation with Unification-Based DeepStochLog. In: Tarek R. Besold; Artur d’Avila Garcez; Ernesto Jimenez-Ruiz; Roberto Confalonieri; Pranava Madhyastha; Benedikt Wagner, Neural-Symbolic Learning and Reasoning 18th International Conference, NeSy 2024, Barcelona, Spain, September 9–12, 2024, Proceedings, Part I. Paper presented at 18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy 2024), Barcelona, Spain, September 9-12, 2024. (pp. 312-330). Springer. [BibTeX]
Hazra, R. & De Raedt, L. (2023). Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. In: Danai Koutra; Claudia Plant; Manuel Gomez Rodriguez; Elena Baralis; Francesco Bonchi, Machine Learning and Knowledge Discovery in Databases: Research Track European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023), Turin, Italy, September 18-22, 2023. (pp. 213-229). Springer. [BibTeX]
De Smet, L. , Zuidberg dos Martires, P. , Manhaeve, R. , Marra, G. , Kimmig, A. & De Raedt, L. (2023). Neural Probabilistic Logic Programming in Discrete-Continuous Domains. In: Robin J. Evans; Ilya Shpitser, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Paper presented at 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, Pennsylvania, USA, July 31 - August 4, 2023. (pp. 529-538). JMLR. [BibTeX]
Yang, W. , Marra, G. , Rens, G. & De Raedt, L. (2023). Safe Reinforcement Learning via Probabilistic Logic Shields. In: Edith Elkind, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023). Paper presented at 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R., August 19-25, 2023. (pp. 5739-5749). International Joint Conferences on Artificial Intelligence. [BibTeX]
Yang, W. , Marra, G. , Rens, G. & De Raedt, L. (2023). Safe Reinforcement Learning via Probabilistic Logic Shields. In: Artur S. d'Avila Garcez; Tarek R. Besold; Marco Gori; Ernesto Jiménez-Ruiz, Neural-Symbolic Learning and Reasoning 2023 Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023. Paper presented at 17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2023), Siena, Italy, July 3-5, 2023. (pp. 428-429). Technical University of Aachen. [BibTeX]
Maene, J. & De Raedt, L. (2023). Soft-Unification in Deep Probabilistic Logic. In: Proceedings of the Conference on Neural Information Processing Systems. Paper presented at 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, December 10-16, 2023. Neural Information Processing Systems Foundation Inc.. [BibTeX]
Winters, T. , Marra, G. , Manhaeve, R. & De Raedt, L. (2022). DeepStochLog: Neural Stochastic Logic Programming. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. Paper presented at 36th AAAI Conference on Artificial Intelligence, (Virtual conference), February 22 - March 1, 2022. (pp. 10090-10100). AAAI Press. [BibTeX]
Winters, T. , Marra, G. , Manhaeve, R. & De Raedt, L. (2022). DeepStochLog: Neural Stochastic Logic Programming (Extended Abstract). In: Yuliya Lierler; Jose F. Morales; Carmine Dodaro; Veronica Dahl; Martin Gebser; Tuncay Tekle, Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022. Paper presented at 38th International Conference on Logic Programming, Haifa, Israel, July 31 - August 6, 2022. (pp. 126-128). Open Publishing Association. [BibTeX]
Verreet, V. , Derkinderen, V. , Zuidberg dos Martires, P. & De Raedt, L. (2022). Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. Paper presented at 36th AAAI Conference on Artificial Intelligence, (Virtual conference), February 22 - March 1, 2022. (pp. 10060-10069). AAAI Press. [BibTeX]
Verreet, V. , Derkinderen, V. , Dos Martires, P. Z. & De Raedt, L. (2022). Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. In: Yuliya Lierler; Jose F. Morales; Carmine Dodaro; Veronica Dahl; Martin Gebser; Tuncay Tekle, Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022. Paper presented at 38th International Conference on Logic Programming, Haifa, Israel, July 31 - August 6, 2022. (pp. 153-155). Open Publishing Association. [BibTeX]
Yang, W. , Jain, A. , De Raedt, L. & Meert, W. (2022). Parameter Learning in ProbLog With Annotated Disjunctions. In: Advances in Intelligent Data Analysis XX 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Paper presented at 20th International Symposium on Intelligent Data Analysis (IDA 2022), Rennes, France, April 20–22, 2022. (pp. 378-391). Springer. [BibTeX]
Manhaeve, R. , Marra, G. & De Raedt, L. (2021). Approximate Inference for Neural Probabilistic Logic Programming. In: Meghyn Bienvenu; Gerhard Lakemeyer; Esra Erdem, Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning Special Session on KR and Machine Learning. Paper presented at 18th International Conference on Principles of Knowledge Representation and Reasoning (KR 2021), (Online conference), November 3-12, 2021. (pp. 475-486). International Joint Conferences on Artificial Intelligence Organization. [BibTeX]
Kumar, M. , Kolb, S. , Gautrais, C. & De Raedt, L. (2021). Democratizing Constraint Satisfaction Problems through Machine Learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. Paper presented at 35th AAAI Conference on Artificial Intelligence (AAAI 2021), (Virtual conference), February 2-9, 2021. (pp. 16057-16059). AAAI Press. [BibTeX]
De Raedt, L. , Dumancic, S. , Manhaeve, R. & Marra, G. (2021). From Statistical Relational to Neuro-Symbolic Artificial Intelligence. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Paper presented at Twenty-Ninth International Joint Conference on Artificial Intelligence(IJCAI 2020), Yokohama, Japan, Januray 7-15, 2021. (pp. 4943-4950). ijcai.org. [BibTeX]
Jain, A. , Gautrais, C. , Kimmig, A. & De Raedt, L. (2021). Learning CNF Theories Using MDL and Predicate Invention. In: Zhi-Hua Zhou, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Paper presented at 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), Montreal, Canada, (Virtual conference), August 19-27, 2021. (pp. 2599-2605). International Joint Conferences on Artificial Intelligence. [BibTeX]
Persson, A. , Martires, P. Z. D. , De Raedt, L. & Loutfi, A. (2021). ProbAnch: a Modular Probabilistic Anchoring Framework. In: Christian Bessiere, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. Paper presented at International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, January 7-15, 2021. (pp. 5285-5287). International Joint Conferences on Artificial Intelligence Organization (IJCAI). [BibTeX]
Kolb, S. , Dos Martires, P. Z. & De Raedt, L. (2020). How to Exploit Structure while Solving Weighted Model Integration Problems. In: UAI 2019 Proceedings. Paper presented at 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019. (pp. 744-754). Association For Uncertainty in Artificial Intelligence (AUAI). [BibTeX]
Derkinderen, V. , Heylen, E. , Zuidberg Dos Martires, P. , Kolb, S. & De Raedt, L. (2020). Ordering Variables for Weighted Model Integration. In: Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI). Paper presented at Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), (virtual online), August 3-6, 2020. (pp. 879-888). AUAI Press. [BibTeX]
Kumar, M. , Teso, S. , De Causmaecker, P. & De Raedt, L. (2019). Automating Personnel Rostering by Learning Constraints Using Tensors. In: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. Paper presented at 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland, Oregon, USA, November 4-6, 2019. (pp. 697-704). IEEE. [BibTeX]
Zuidberg Dos Martires, P. , Dries, A. & De Raedt, L. (2019). Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation. In: Proceedings of the AAAI Conference on Artificial Intelligence. Paper presented at 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Hawaii, January 27 - February 1, 2019. (pp. 7825-7833). AAAI Press. [BibTeX]
Can, O. A. , Zuidberg Dos Martires, P. , Persson, A. , Gaal, J. , Loutfi, A. , De Raedt, L. , Yuret, D. & Saffiotti, A. (2019). Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations. In: Archna Bhatia, Yonatan Bisk, Parisa Kordjamshidi, Jesse Thomason, Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP). Paper presented at Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP), Minneapolis, Minnesota, USA, June, 2019. (pp. 29-39). Association for Computational Linguistics. [BibTeX]
Kolb, S. , Morettin, P. , Zuidberg Dos Martires, P. , Sommavilla, F. , Passerini, A. , Sebastiani, R. & De Raedt, L. (2019). The pywmi framework and toolbox for probabilistic inference using weighted model integration. In: Sarit Kraus, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Paper presented at 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macau, China, August 10-16, 2019. (pp. 6530-6532). AAAI Press. [BibTeX]
Verbruggen, G. & De Raedt, L. (2018). Automatically Wrangling Spreadsheets into Machine Learning Data Formats. In: Wouter Duivesteijn, Arno Siebes, Antti Ukkonen, Advances in Intelligent Data Analysis XVII. Paper presented at 17th International Symposium on Intelligent Data Analysis (IDA 2018), ’s-Hertogenbosch, The Netherlands, October 24–26, 2018. (pp. 367-379). Springer. [BibTeX]
Kumar, M. , Teso, S. , De Causmaecker, P. & De Raedt, L. (2018). Automating Personnel Rostering by Learning Constraints Using Tensors. Paper presented at DSO Workshop - IJCAI, Stockholm, Sweden, July 13-19, 2018. [BibTeX]
Manhaeve, R. , Dumancic, S. , Kimmig, A. , Demeester, T. & De Raedt, L. (2018). DeepProbLog: Neural Probabilistic Logic Programming. In: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett, Advances in Neural Information Processing Systems 31 (NIPS 2018). Paper presented at 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada, December 2-8, 2018. (pp. 3753-3760). Neural Information Processing Systems Foundation Inc.. [BibTeX]
De Raedt, L. , Blockeel, H. , Kolb, S. , Teso, S. & Verbruggen, G. (2018). Elements of an Automatic Data Scientist. In: Wouter Duivesteijn, Arno Siebes, Antti Ukkonen, Advances in Intelligent Data Analysis XVII. Paper presented at 17th International Symposium (IDA 2018), ’s-Hertogenbosch, The Netherlands, October 24–26, 2018. Cham: Springer International Publishing. [BibTeX]
De Raedt, L. , Passerini, A. & Teso, S. (2018). Learning Constraints from Examples. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Thirtieth Innovative Applications of Artificial Intelligence Conference, Eigth Symposium on Educational Advances in Artificial Intelligence 2-7 February 2018, New Orleans, Louisiana, USA. Paper presented at 32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Los Angeles, USA, February 2-7, 2018. (pp. 7965-7970). CA AAAI Press. [BibTeX]
Kolb, S. , Teso, S. , Passerini, A. & De Raedt, L. (2018). Learning SMT(LRA) Constraints using SMT Solvers. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Paper presented at 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, July 13-19, 2018. (pp. 2333-2340). AAAI Press. [BibTeX]
Antanas, L. , Dries, A. , Moreno, P. & De Raedt, L. (2018). Relational Affordance Learning for Task-Dependent Robot Grasping. In: Nicolas Lachiche, Christel Vrain, Inductive Logic Programming 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers. Paper presented at 27th International Conference (ILP 2017), Orléans, France, September 4-6, 2017. (pp. 1-15). Cham: Springer International Publishing. [BibTeX]
Paramonov, S. , Bessiere, C. , Dries, A. & De Raedt, L. (2018). Sketched Answer Set Programming. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). Paper presented at 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, November 5-7, 2018. (pp. 694-701). IEEE. [BibTeX]
De Raedt, L. (2017). Constraint Learning and Dynamic Probabilistic Programming. In: Formal methods and machine learning. Paper presented at Formal Methods and Machine Learning Seminar, Dagstuhl Seminar 17351, Wadern, Germany, August 27-September 1, 2017. [BibTeX]
De Raedt, L. (2017). Learning constraints and formula's for spreadsheets. In: Approaches and Applications of Inductive Programming. Paper presented at Approaches and Applications of Inductive Programming Seminar, Schloss Dagstuhl – Leibniz Center for Informatics, Wadern, Germany, September 17-20, 2017. [BibTeX]
Kolb, S. , Paramonov, S. , Guns, T. & De Raedt, L. (2017). Learning constraints in spreadsheets and tabular data. Paper presented at Inductive Logic Programming conference (ILP 2017), Orléans, France, September 4-6, 2017. [BibTeX]
Kimming, A. & De Raedt, L. (2017). Probabilistic Logic Programs: Unifying Program Trace and Possible World Semantics. Paper presented at Workshop on probabilistic programming semantics (PPS 2017), Paris, France, January 15-21, 2017. [BibTeX]
De Raedt, L. (2017). Probabilistic Programming and its Applications. In: Gabriele Kern-Isberner; Johannes Fürnkranz; Matthias Thimm, KI 2017: Advances in Artificial Intelligence 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings. Paper presented at 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017. Springer. [BibTeX]
Dries, A. , Davis, J. , Belle, V. & De Raedt, L. (2017). Solving Probability Problems in Natural Language. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Paper presented at 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, August 19-25, 2017. (pp. 3981-3987). AAAI Press. [BibTeX]
Babaki, B. , Guns, T. & De Raedt, L. (2017). Stochastic Constraint Programming with And-Or Branch-and-Bound. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Paper presented at 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, August 19-25, 2017. (pp. 539-545). AAAI Press. [BibTeX]
Paramonov, S. , Kolb, S. , Guns, T. & De Raedt, L. (2017). TaCLe: Learning Constraints in Tabular Data. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Paper presented at 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), Pan Pacific Singapore Hotel, Singapore, Singapore, November 6-10, 2017. (pp. 2511-2514). New York: Association for Computing Machinery. [BibTeX]
Verbruggen, G. & De Raedt, L. (2017). Towards automated relational data wrangling. In: Proceedings of AutoML2017 @ ECML-PKDD: Automatic selection, configuration and composition of machine learning algorithms. Paper presented at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Makedonia, September 18-22, 2017. (pp. 18-26). Technical University of Aachen. [BibTeX]
Paramonov, S. , van Leuween, M. , Denecker, M. & De Raedt, L. (2016). An Exercise in Declarative Modeling for Relational Query Mining. In: Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto, Inductive Logic Programming 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers. Paper presented at 25th International Conference on Inductive Logic Programming (ILP 2015), Kyoto, Japan, August 20-22, 2015. (pp. 166-182). Springer. [BibTeX]
De Raedt, L. (2016). An Introduction to Hybrid Probabilistic (Logic) Programming. Paper presented at Hybrid Reasoning for Intelligent Systems, Freiburg, Germany, June 6-7, 2016. [BibTeX]
De Raedt, L. (2016). Can we automate data science?. In: European Data Science Conference November 07-08, 2016 in Luxembourg. Paper presented at The European Data Science Conference (EDSC 2016), Luxenbourg, Luxenbourg, November 7-8, 2016. (pp. 38-38). [BibTeX]
Vlasselaer, J. , Kimmig, A. , Dries, A. , Meert, W. & De Raedt, L. (2016). Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. In: Proceedings of the First Workshop on Beyond NP. Paper presented at The AAAI-16 Workshop on Beyond NP, Phoenix, Arizona, USA, February 12-13, 2016. (pp. 359-364). Association for the Advancement of Artificial Intelligence. [BibTeX]
Orsini, F. , Frasconi, P. & De Raedt, L. (2016). kProbLog: An Algebraic Prolog for Kernel Programming. In: Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto, Inductive Logic Programming 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers. Paper presented at 25th International Conference on Inductive Logic Programming (ILP 2015), Kyoto, Japan, August 20-22, 2015. (pp. 152-165). Springer. [BibTeX]
Nitti, D. , Ravkic, I. , Davis, J. & De Raedt, L. (2016). Learning the structure of dynamic hybrid relational models. In: Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen, ECAI 2016 Proceedings. Paper presented at 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, The Netherlands, September 29 - October 2, 2016. (pp. 1283-1290). IOS Press. [BibTeX]
De Raedt, L. (2016). On the history and future of machine learning: A personal interpretation and perspective. Paper presented at The Benelearn - Belgian - Dutch Conference on Machine Learning, Kortrijk, Belgium, September 12-13, 2016. [BibTeX]
De Raedt, L. (2016). Probabilistic Programs and Their Applications. In: 4th Conference of SANKEN Core to Core Program Proceedings. Paper presented at 4th Conference of SANKEN Core to Core Program, Osaka, Japan, December 13-14, 2016. [BibTeX]
Vercruyssen, V. , De Raedt, L. & Davis, J. (2016). Qualitative spatial reasoning for soccer pass prediction. In: Jan Van Haaren, Mehdi Kaytoue, Jesse Davis, Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016). Paper presented at Machine Learning and Data Mining for Sports Analytics (MLSA 2016) @ ECML/PKDD 2016, Riva del Garda, Italy, September 19, 2016. Technical University of Aachen. [BibTeX]
Antanas, L. , Moreno, P. & De Raedt, L. (2016). Relational Kernel-Based Grasping with Numerical Features. In: Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto, Inductive Logic Programming 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers. Paper presented at 25th International Conference on Inductive Logic Programming (ILP 2015), Kyoto, Japan, August 20-22, 2015. (pp. 1-14). Springer. [BibTeX]
De Raedt, L. (2016). Towards synthesising inductive data models. Paper presented at Data Science Summit, Venice, Italy, September 14-17, 2016. [BibTeX]
Vlasselae, J. , Van den Broeck, G. , Kimmig, A. , Meert, W. & De Raedt, L. (2015). Anytime Inference in Probabilistic Logic Programs with TP-Compilation. In: Qiang Yang; Michael Wooldridge, Proceedings of 24th International Joint Conference on ArtificialIntelligence (IJCAI). Paper presented at 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31, 2015. (pp. 1852-1858). AAAI Press. [BibTeX]
Babaki, B. , Guns, T. , Nijssen, S. & De Raedt, L. (2015). Constraint-Based Querying for Bayesian Network Exploration. In: Elisa Fromont, Tilj De Bie, Matthijs van Leeuwen, Advances in Intelligent Data Analysis XIV 14th International Symposium, IDA 2015, Saint Etienne, France, October 22 -24, 2015. Proceedings. Paper presented at 14th International Symposium on Intelligent Data Analysis (IDA 2015), Saint Etienne, France, October 22-24, 2015. (pp. 13-24). Cham: Springer International Publishing. [BibTeX]
De Raedt, L. (2015). Declarative Machine Learning and Data Mining. In: Constraint programming for Analytics Workshop. Paper presented at The Workshop at 21st International Conference on Principles and Practice of Constraint Programming (CP 2015), Cork, Ireland, August 31 - September 4, 2015. [BibTeX]
Orsini, F. , Frasconi, P. & De Raedt, L. (2015). Graph Invariant Kernels. In: Wooldridge M.; Yang Q., Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Paper presented at 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31, 2015. (pp. 3756-3762). Palo Alto: AAAI Press. [BibTeX]
De Raedt, L. , Dries, A. , Thon, I. , Van den Broeck, G. & Verbeke, M. (2015). Inducing Probabilistic Relational Rules from Probabilistic Examples. In: Wooldridge M.; Yang Q., Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Paper presented at 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31, 2015. (pp. 1835-1842). Palo Alto: AAAI Press. [BibTeX]
Frasconi, P. , Costa, F. , De Raedt, L. & De Grave, K. (2015). kLog: A language for logical and relational learning with kernels. In: Wooldridge M.; Yang Q., Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015). Paper presented at 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31, 2015. (pp. 4183-4187). AAAI Press. [BibTeX]
De Raedt, L. (2015). Languages for Learning and Mining. In: B. Bonet; S. Koenig, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Paper presented at 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference (IAAI 2015), Austin, United States, January 25-30, 2015. (pp. 4107-4111). AAAI Press. [BibTeX]
d'Avila Garcez, A. , Besold, T. R. , De Raedt, L. , Földiák, P. , Hitzler, P. , Icard, T. , Kiihnberger, K. , Lamb, L. C. & et al. (2015). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. In: Knowledge Representation and Reasoning Integrating Symbolic and Neural Approaches - Papers from the 2015 AAAI Spring Symposium, Technical Report. Paper presented at AAAI Spring Symposium - Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Stanford University, Palo Alto, CA, USA, March 23-25, 2015. (pp. 18-21). AAAI Press. [BibTeX]
Van Daele, D. , Kimmig, A. & De Raedt, L. (2015). PageRank, ProPPR, and Stochastic Logic Programs. In: Jesse Davis; Jan Ramon, Inductive Logic Programming 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers. Paper presented at 24th International Conference on Inductive Logic Programming (ILP 2014), Nancy, France, September 14-16, 2014. (pp. 168-180). Cham: Springer. [BibTeX]
Nitti, D. , Belle, V. & De Raedt, L. (2015). Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. In: Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares, Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Paper presented at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 7-11, 2015. (pp. 327-342). Springer. [BibTeX]
De Raedt, L. (2015). Probabilistic programming and its applications (Keynote Abstract). In: Antonis Bikakis, Xianghan Zheng, Multi-disciplinary Trends in Artificial Intelligence 9th International Workshop, MIWAI 2015, Fuzhou, China, November 13-15, 2015, Proceedings. Paper presented at 9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015), Fuzhou, China, November 13-15, 2015. (pp. xiii-xiv). Cham: Springer International Publishing. [BibTeX]
Dries, A. , Kimmig, A. , Meert, W. , Renkens, J. , Van den Broeck, G. , Vlasselaer, J. & De Raedt, L. (2015). ProbLog2: Probabilistic Logic Programming. In: Albert Bifet, Michael May, Bianca Zadrozny, Ricard Gavalda, Dino Pedreschi, Francesco Bonchi, Jaime Cardoso, Myra Spiliopoulou, Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 7-11, 2015. (pp. 312-315). Springer. [BibTeX]
Le Van, T. , van Leuween, M. , Nijssen, S. & De Raedt, L. (2015). Rank Matrix Factorisation. In: Tru Cao; Ee-Peng Lim; Zhi-Hua Zhou; Tu-Bao Ho; David Cheung; Hiroshi Motoda, Advances in Knowledge Discovery and Data Mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I. Paper presented at 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Ho Chi Minh City, Viet Nam, May 19-22,2015. (pp. 734-746). Cham: Springer. [BibTeX]
De Raedt, L. (2015). Towards probabilistic inductive programming synthesis. In: Approaches and Applications ofInductive Programming. Paper presented at Approaches and Applications of Inductive Programming Seminar, Dagstuhl, Germany, October 25-30, 2015. [BibTeX]
De Raedt, L. (2015). Using and developing declarative languages for machine learning and data mining. In: Marina De Vos; Thomas Eiter; Yuliya Lierler; Francesca Toni, Technical Communications of ICLP Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015). Paper presented at 31st International Conference on Logic Programming (ICLP 2015), Cork, Ireland, August 31 - September 4, 2015. Technical University of Aachen. [BibTeX]
Vlasselaer, J. , Meert, W. , Van den Broeck, G. & De Raedt, L. (2014). AAAI Workshop - Technical Report. In: Papers from the 2014 AAAI Workshop. Paper presented at International Workshop on Statistical Relational AI, Quebec City, Canada, July 27, 2014. (pp. 131-134). AAAI Press. [BibTeX]
Nitti, D. , Chliveros, G. , Pateraki, M. , De Raedt, L. , Hourdakis, E. & Trahanias, P. (2014). Application of Dynamic Distributional Clauses for Multi-hypothesis Initialization in Model-based Object Tracking. In: Sebastiano Battiato, Proceedings of the 9th International Conference on Computer Vision Theory and Applications - (Volume 1). Paper presented at 9th International Conference on Computer Vision Theory and Applications, (VISAPP 2014), Lisbon, Portugal, January 5-8, 2014. (pp. 256-261). SciTePress. [BibTeX]
Vlasselaer, J. , Renkens, J. , Van den Broeck, G. & De Raedt, L. (2014). Compiling Probabilistic Logic Programs into Sentential Decision Diagrams. In: Workshop on Probabilistic Logic Programming (PLP) Proceedings. Paper presented at 1st International Workshop on Probabilistic Logic Programming (PLP 2014), Vienna, Austria, July 17, 2014. (pp. 1-10). [BibTeX]
Vlasselaer, J. , Meert, W. , Langone, R. & De Raedt, L. (2014). Condition monitoring with incomplete observations. In: Torsten Schaub; Gerhard Friedrich; Barry O'Sullivan, ECAI 2014: 21st European Conference on Artificial Intelligence 18-22 August 2014, Prague, Czech Republic Proceedings. Paper presented at 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, August 18-22, 2014. (pp. 1215-1216). IOS Press. [BibTeX]
Nitti, D. , De Lact, T. & De Raedt, L. (2014). Distributional Clauses Particle Filter. In: Toon Calders; Floriana Esposito; Eyke Hüllermeier; Rosa Meo, Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III. Paper presented at European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Nancy, France, September 15-19, 2014. (pp. 504-507). Berlin: Springer Berlin/Heidelberg. [BibTeX]
Renkens, J. , Kimmig, A. , Van den Broeck, G. & De Raedt, L. (2014). Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. Paper presented at 28th AAAI Conference on Artificial Intelligence (AAAI 2014), 26th Innovative Applications of Artificial Intelligence Conference (IAAI 2014) and the 5th Symposium on Educational Advances in Artificial Intelligence (EAAI 2014), Quebec City, Canada, July 27-31, 2014. (pp. 2490-2496). AAAI Press. [BibTeX]
Verbeke, M. , Frasconi, P. , De Grave, K. , Costa, F. & De Raedt, L. (2014). kLogNLP: Graph Kernel–based Relational Learning of Natural Language. In: K. Bontcheva; Z. Jingbo, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics System Demonstrations. Paper presented at 52nd Annual Meeting of the Association-for-Computational-Linguistics (ACL), Baltimore, Maryland, USA, June 22-27, 2014. (pp. 85-90). Association for Computational Linguistics. [BibTeX]
Verbeke, M. , Van Asch, V. , Daelemans, W. & De Raedt, L. (2014). Lazy and Eager Relational Learning Using Graph-Kernels. In: Laurent Besacier, Adrian-Horia Dediu, Carlos Martín-Vide, Statistical Language and Speech Processing Second International Conference, SLSP 2014, Grenoble, France, October 14-16, 2014, Proceedings. Paper presented at 2nd International Conference on Statistical Language and Speech Processing (SLSP 2014), Grenoble, France, October 14-16, 2014. (pp. 171-184). Cham: Springer. [BibTeX]
De Raedt, L. , Dries, A. , Guns, T. & Bessiere, C. (2014). Learning constraint satisfaction problems: An ILP perspective. Paper presented at 24th International Conference on Inductive Logic Programming (ILP 2014), Nancy, France, September 14-16, 2014. (pp. 1-6). [BibTeX]
Moldovan, B. & De Raedt, L. (2014). Learning relational affordance models for two-arm robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Paper presented at 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), Palmer House Hilton Hotel, Chicago, United States, September 14-18, 2014. (pp. 2916-2922). IEEE Press. [BibTeX]
Moldovan, B. & De Raedt, L. (2014). Occluded object search by relational affordances. In: 2014 IEEE International Conference on Robotics & Automation (ICRA). Paper presented at IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, May 31 - June 7, 2014. (pp. 169-174). IEEE. [BibTeX]
Van Daele, D. , Kimmig, A. & De Raedt, L. (2014). PageRank, ProPPR, and Stochastic Logic Programs. In: Jesse Davis; Jan Ramon, Inductive Logic Programming 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers. Paper presented at 24th International Conference on Inductive Logic Programming (ILP 2014), Vienna, Austria, July 14-16, 2014. (pp. 168-180). Springer. [BibTeX]
Le Van, T. , van Leuween, M. , Nijssen, S. , Fierro, A. C. , Marchal, K. & De Raedt, L. (2014). Ranked Tiling. In: Toon Calders; Floriana Esposito; Eyke Hüllermeier; Rosa Meo, Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II. Paper presented at European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Nancy, France, September 15-19, 2014. (pp. 98-113). Springer. [BibTeX]
Nitti, D. , De Laet, T. & De Raedt, L. (2014). Relational object tracking and learning. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). Paper presented at 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong Convention and Exhibition Center, Hong Kong, China, May 31 - June 7, 2014. IEEE. [BibTeX]
Costa, F. , Verbeke, M. & De Raedt, L. (2014). Relational Regularization and Feature Ranking. In: M. Zaki; Z. Obradovic; P. Ning Tan; A. Banerjee; C. Kamath; S. Parthasarathy, Proceedings of the 2014 SIAM International Conference on Data Mining (SDM). Paper presented at 14th SIAM International Conference on Data Mining (SDM 2014), Philadephia, Pennsylvania, USA, April 24-26, 2014. (pp. 650-658). Society for Industrial and Applied Mathematics Publications. [BibTeX]
Oramas, J. M. , De Raedt, L. & Tuytelaars, T. (2014). Towards Cautious Collective Inference for Object Verification. In: IEEE Workshop on Applications of Computer Vision (WACV). Paper presented at 2014 IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, USA, March 24-26, 2014. (pp. 269-276). IEEE. [BibTeX]
Theobald, M. , De Raedt, L. , Dylla, M. , Kimmig, A. & Miliaraki, I. (2013). 10 Years of Probabilistic Querying: What Next?. In: Barbara Catania; Giovanna Guerrini; Jaroslav Pokorný, Advances in Databases and Information Systems 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings. Paper presented at 17th East-European Conference on Advances in Databases and Information Systems (ADBIS 2013), Genoa, Italy, September 1-4, 2013. (pp. 1-13). Springer. [BibTeX]
Nitti, D. , De Laet, T. & De Raedt, L. (2013). A particle filter for hybrid relational domains. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Paper presented at 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon (IROS 2013), Tokyo, Japan, November 3-8, 2013. (pp. 2764-2771). IEEE Press. [BibTeX]
Antanas, L. , Hoffmann, M. , Frasconi, P. , Tuytelaars, T. & De Raedt, L. (2013). A relational kernel-based approach to scene classification. In: Proceedings of IEEE Workshop on Applications of Computer Vision. Paper presented at 2013 IEEE Workshop on Applications of Computer Vision (WACV 2013), Clearwater Beach, Florida, USA, January 15-17, 2013. (pp. 133-139). IEEE. [BibTeX]
Dzyuba, V. , van Leuween, M. , Nijssen, S. & De Raedt, L. (2013). Active Preference Learning for Ranking Patterns. In: 25th International Conference on Tools with Artificial Intelligence ICTA I2013 Proceedings. Paper presented at 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2013), Washington DC, USA, November 4-6, 2013. (pp. 532-539). IEEE. [BibTeX]
Oramas, J. M. , De Raedt, L. & Tuytelaars, T. (2013). Allocentric Pose Estimation. In: 2013 IEEE International Conference on Computer Vision. Paper presented at 14th IEEE International Conference on Computer Vision (ICCV 2013), Sydney, Australia, December 3-6, 2013. (pp. 289-296). IEEE. [BibTeX]
Moldovan, B. , Thon, I. , Davis, J. & De Raedt, L. (2013). Estimation of Conditional Probabilities in Probabilistic Programming Languages. In: van der Gaag, Linda C., Symbolic and Quantitative Approaches to Reasoning with Uncertainty 12th European Conference, ECSQARU 2013, Utrecht, The Netherlands, July 8-10, 2013. Proceedings. Paper presented at 12th European Conference (ECSQARU 2013), Utrecht, The Netherlands, July 8-10, 2013. (pp. 436-448). Springer. [BibTeX]
Guns, T. , Tack, G. , Nijssen, S. & De Raedt, L. (2013). MiningZinc: A Modeling Language for Constraint-based Mining. In: Francesca Rossi, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Paper presented at Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013. (pp. 1365-1372). AAAI Press. [BibTeX]
De Raedt, L. , Paramono, S. & van Leeuwen, M. (2013). Relational Decomposition using Answer Set Programming. Paper presented at Workshop on Learning and Nonmonotonic Reasoning, La Coruna, Spain, September 15, 2013. [BibTeX]
De Raedt, L. , Paramono, S. & van Leeuwen, M. (2013). Relational Decomposition using Answer Set Programming. In: Online Preprints 23rd International Conference on Inductive Logic Programming. Paper presented at 23rd International Conference on Inductive Logic Programming (ILP 2013), Rio de Janeiro, Brazil, August 28-30, 2013. [BibTeX]
Guns, T. , Dries, A. , Tack, G. , Nijssen, S. & De Raedt, L. (2013). The MiningZinc Framework for Constraint-Based Itemset Mining. In: Ding, W; Washio, T; Xiong, H; Karypis, G; Thuraisingham, B; Cook, D; Wu, X, 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). Paper presented at 13th IEEE International Conference on Data Mining Workshops (ICDMW 2013), Dallas, Texas, USA, December 7-10, 2013. (pp. 1081-1084). IEEE. [BibTeX]

Conference proceedings (editor)

De Raedt, L. (ed.) (2022). Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI).. International Joint Conferences on Artificial Intelligence (Proceedings of the International Joint Conference on Artificial Intelligence 31). [BibTeX]
Quyet Thang, H. (ed.) , Le Anh, P. (ed.) , De Raedt, L. (ed.) , Deville, Y. (ed.) , Bui, M. (ed.) , Truong Thi Dieu, L. (ed.) , Nguyen Thi, O. (ed.) , Dinh Viet, S. (ed.) & Nguyen Ba, N. (ed.) (2015). Proceedings of the Sixth International Symposium on Information and Communication Technology. New York: Association for Computing Machinery. [BibTeX]