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J. Vlasselaer, G. Van den Broeck, A. Kimmig, W. Meert and L. De Raedt. Tp-compilation for inference inprobabilistic logic programs. International Journal of Approximate Reasoning, 78:15-32, [ BibTeX | DiVA ] |
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G. Marra, S. Dumancic, R. Manhaeve and L. De Raedt. From statistical relational to neurosymbolic artificial intelligence : A survey. Artificial Intelligence, 328, 2024 [ BibTeX | DiVA ] |
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G. Venturato, V. Derkinderen, P. Zuidberg dos Martires and L. De Raedt. Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, 38(38:18):20567-20576, 2024 [ BibTeX | DiVA ] |
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V. Verreet, L. De Raedt and J. Bekker. Modeling PU learning using probabilistic logic programming. Machine Learning, 113(3):1351-1372, 2024 [ BibTeX | DiVA ] |
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R. Hazra, P. Zuidberg dos Martires and L. De Raedt. SayCanPay : Heuristic Planning with Large Language Models Using Learnable Domain Knowledge. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, 38(38:18):20123-20133, 2024 [ BibTeX | DiVA ] |
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V. Derkinderen, R. Manhaeve, P. Zuidberg dos Martires and L. De Raedt. Semirings for probabilistic and neuro-symbolic logic programming. International Journal of Approximate Reasoning, 171, 2024 [ BibTeX | DiVA ] |
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Y. Jiao, L. De Raedt and G. Marra. Valid Text-to-SQL Generation with Unification-Based DeepStochLog. In Neural-Symbolic Learning and Reasoning : 18th International Conference, NeSy 2024, Barcelona, Spain, September 9–12, 2024, Proceedings, Part I, 14979(Vol. 14979):312-330, 2024 [ BibTeX | DiVA ] |
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M. Van Roy, P. Robberechts, W. C. Yang, L. De Raedt and J. Davis. A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer. The journal of artificial intelligence research, 77:517-562, 2023 [ BibTeX | DiVA ] |
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R. Hazra and L. De Raedt. Deep Explainable Relational Reinforcement Learning : A Neuro-Symbolic Approach. In Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV, 14172(14172):213-229, 2023 [ BibTeX | DiVA | PDF ] |
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L. De Smet, P. Zuidberg dos Martires, R. Manhaeve, G. Marra, A. Kimmig and L. De Raedt. Neural Probabilistic Logic Programming in Discrete-Continuous Domains. In Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, pages 529-538, 2023 [ BibTeX | DiVA | PDF ] |
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W. C. Yang, G. Marra, G. Rens and L. De Raedt. Safe Reinforcement Learning via Probabilistic Logic Shields. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), pages 5739-5749, 2023 [ BibTeX | DiVA ] |
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W. C. Yang, G. Marra, G. Rens and L. De Raedt. Safe Reinforcement Learning via Probabilistic Logic Shields. In 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, 3432(3432):428-429, 2023 [ BibTeX | DiVA ] |
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J. Maene and L. De Raedt. Soft-Unification in Deep Probabilistic Logic. In Proceedings of the Conference on Neural Information Processing Systems, 36(36), 2023 [ BibTeX | DiVA ] |
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T. De Bie, L. De Raedt, J. Hernandez-Orallo, H. H. Hoos, P. Smyth and C. K. Williams. Automating Data Science. Communications of the ACM, 65(3):76-87, 2022 [ BibTeX | DiVA ] |
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T. Winters, G. Marra, R. Manhaeve and L. De Raedt. DeepStochLog : Neural Stochastic Logic Programming. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 36:9:10090-10100, 2022 [ BibTeX | DiVA ] |
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T. Winters, G. Marra, R. Manhaeve and L. De Raedt. DeepStochLog : Neural Stochastic Logic Programming (Extended Abstract). In Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022, 364(364):126-128, 2022 [ BibTeX | DiVA ] |
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V. Verreet, V. Derkinderen, P. Zuidberg dos Martires and L. De Raedt. Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 36:9:10060-10069, 2022 [ BibTeX | DiVA ] |
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V. Verreet, V. Derkinderen, P. Z. Dos Martires and L. De Raedt. Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. In Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022, 364(364):153-155, 2022 [ BibTeX | DiVA ] |
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W. C. Yang, J. F. Raskin and L. De Raedt. Lifted model checking for relational MDPs. Machine Learning, pages 3797-3838, 2022 [ BibTeX | DiVA ] |
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R. Manhaeve, G. Marra, T. Demeester, S. Dumancic, A. Kimmig and L. De Raedt. Neuro-Symbolic AI = Neural + Logical + Probabilistic AI. In Neuro-Symbolic Artificial Intelligence: The State of the Art, pages 173-191, 2022 [ BibTeX | DiVA ] |
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W. c. Yang, A. Jain, L. De Raedt and W. Meert. 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, pages 378-391, 2022 [ BibTeX | DiVA ] |
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R. Dechter and L. De Raedt. Preface. IJCAI International Joint Conference on Artificial Intelligence, pages lvi-lvii, 2022 [ BibTeX | DiVA ] |
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R. Dechter, L. De Raedt, C. Sierra, M. Ortiz and S. Woltran. WELCOME TO IJCAI-ECAI 2022. IJCAI International Joint Conference on Artificial Intelligence 2022 [ BibTeX | DiVA ] |
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R. Manhaeve, G. Marra and L. De Raedt. Approximate Inference for Neural Probabilistic Logic Programming. In Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning : Special Session on KR and Machine Learning, pages 475-486, 2021 [ BibTeX | DiVA ] |
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M. Kumar, S. Kolb, C. Gautrais and L. De Raedt. Democratizing Constraint Satisfaction Problems through Machine Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 35:18(35(18)):16057-16059, 2021 [ BibTeX | DiVA ] |
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L. De Raedt, S. Dumancic, R. Manhaeve and G. Marra. From Statistical Relational to Neuro-Symbolic Artificial Intelligence. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pages 4943-4950, 2021 [ BibTeX | DiVA ] |
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A. Jain, C. Gautrais, A. Kimmig and L. De Raedt. Learning CNF Theories Using MDL and Predicate Invention. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pages 2599-2605, 2021 [ BibTeX | DiVA ] |
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R. Manhaeve, S. Dumančić, A. Kimmig, T. Demeester and L. De Raedt. Neural probabilistic logic programming in DeepProbLog. Artificial Intelligence, 298, 2021 [ BibTeX | DiVA ] |
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S. Kolb, P. Z. Dos Martires and L. De Raedt. How to Exploit Structure while Solving Weighted Model Integration Problems. In UAI 2019 Proceedings, 262:744-754, 2020 [ BibTeX | DiVA ] |
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V. Derkinderen, E. Heylen, P. Zuidberg Dos Martires, S. Kolb and L. De Raedt. Ordering Variables for Weighted Model Integration. In Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI), 124(124):879-888, 2020 [ BibTeX | DiVA ] |
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S. Kolb, S. Teso, A. Dries and L. De Raedt. Predictive spreadsheet autocompletion with constraints. Machine Learning, 109(2):307-325, 2020 [ BibTeX | DiVA ] |
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V. Belle and L. De Raedt. Semiring programming : A semantic framework for generalized sum product problems. International Journal of Approximate Reasoning, 126:181-201, 2020 [ BibTeX | DiVA ] |
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P. Zuidberg Dos Martires, N. Kumar, A. Persson, A. Loutfi and L. De Raedt. Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring. Frontiers in Robotics and AI, 7, 2020 [ BibTeX | DiVA ] |
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P. Zuidberg dos Martires, V. Derkinderen, L. De Raedt and M. Krantz. Automated Reasoning in Systems Biology : A Necessity for Precision Medicine. In Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, pages 974-980, 2024 [ BibTeX | DiVA ] |
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P. Zuidberg dos Martires, L. De Raedt and A. Kimmig. Declarative probabilistic logic programming in discrete-continuous domains. Artificial Intelligence, 337, 2024 [ BibTeX | DiVA ] |
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O. A. Can, P. Zuidberg Dos Martires, A. Persson, J. Gaal, A. Loutfi, L. De Raedt, D. Yuret and A. Saffiotti. Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations. In Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP), pages 29-39, 2019 [ BibTeX | DiVA ] |
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A. Groß, B. Kracher, J. M. Kraus, S. D. Kühlwein, A. S. Pfister, S. Wiese, K. Luckert, O. Pötz, T. Joos, D. Van Daele, L. De Raedt, M. Kühl and H. A. Kestler. Representing Dynamic Biological Networks With Multi-Scale Probabilistic Models. Communications Biology, 2, 2019 [ BibTeX | DiVA ] |
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L. Antanas, P. Moreno, M. Neumann, R. Pimentel de Figueiredo, K. Kersting, J. Santos-Victor and L. De Raedt. Semantic and geometric reasoning for robotic grasping : a probabilistic logic approach. Autonomous Robots, 43(6):1393-1418, 2019 [ BibTeX | DiVA ] |
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S. Kolb, P. Morettin, P. Zuidberg Dos Martires, F. Sommavilla, A. Passerini, R. Sebastiani and L. De Raedt. The pywmi framework and toolbox for probabilistic inference using weighted model integration. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pages 6530-6532, 2019 [ BibTeX | DiVA ] |
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G. Verbruggen and L. De Raedt. Automatically Wrangling Spreadsheets into Machine Learning Data Formats. In Advances in Intelligent Data Analysis XVII, pages 367-379, 2018 [ BibTeX | DiVA ] |
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M. Kumar, S. Teso, P. De Causmaecker and L. De Raedt. Automating Personnel Rostering by Learning Constraints Using Tensors. 2018 [ BibTeX | DiVA ] |
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R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester and L. De Raedt. DeepProbLog : Neural Probabilistic Logic Programming. In Advances in Neural Information Processing Systems 31 (NIPS 2018), pages 3753-3760, 2018 [ BibTeX | DiVA ] |
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L. De Raedt, H. Blockeel, S. Kolb, S. Teso and G. Verbruggen. Elements of an Automatic Data Scientist. In Advances in Intelligent Data Analysis XVII, 11191(11191), 2018 [ BibTeX | DiVA ] |
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L. De Raedt, A. Passerini and S. Teso. 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, pages 7965-7970, 2018 [ BibTeX | DiVA ] |
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S. Kolb, S. Teso, A. Passerini and L. De Raedt. Learning SMT(LRA) Constraints using SMT Solvers. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pages 2333-2340, 2018 [ BibTeX | DiVA ] |
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L. Antanas, A. Dries, P. Moreno and L. De Raedt. Relational Affordance Learning for Task-Dependent Robot Grasping. In Inductive Logic Programming : 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers, 10759(10759):1-15, 2018 [ BibTeX | DiVA ] |
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B. Moldovan, P. Moreno, D. Nitti, J. Santos-Victor and L. De Raedt. Relational affordances for multiple-object manipulation. Autonomous Robots, 42(1):19-44, 2018 [ BibTeX | DiVA ] |
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S. Paramonov, C. Bessiere, A. Dries and L. De Raedt. Sketched Answer Set Programming. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pages 694-701, 2018 [ BibTeX | DiVA ] |
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A. Kimmig, G. Van den Broeck and L. De Raedt. Algebraic Model Counting. Journal of Applied Logic, 22:46-62, 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Constraint Learning and Dynamic Probabilistic Programming. In Formal methods and machine learning 2017 [ BibTeX | DiVA ] |
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J. Oramas, L. De Raedt and T. Tuytelaars. Context-based Object Viewpoint Estimation : A 2D Relational Approach. Computer Vision and Image Understanding, 160:100-113, 2017 [ BibTeX | DiVA ] |
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L. De Raedt, M. Bui, Y. Deville and T. Dieu-Linh. Editors' Introduction to the Special Issue on ‟Information and Communication Technology”. Informatica - journal of computing and informatics, 41(2):131-131, 2017 [ BibTeX | DiVA ] |
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V. Dzyuba, M. van Leeuwen and L. De Raedt. Flexible constrained sampling with guarantees for pattern mining. Data mining and knowledge discovery, 31:1266-1293, 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Inductive Logic Programming. In Encyclopedia of machine learning and data mining 2017 [ BibTeX | DiVA ] |
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F. Orsini, P. Frasconi and L. De Raedt. kProbLog : an algebraic Prolog for machine learning. Machine Learning, pages 1933-1969, 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Learning constraints and formula's for spreadsheets. In Approaches and Applications of Inductive Programming 2017 [ BibTeX | DiVA ] |
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S. Kolb, S. Paramonov, T. Guns and L. De Raedt. Learning constraints in spreadsheets and tabular data. Machine Learning, pages 1441-1468, 2017 [ BibTeX | DiVA ] |
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S. Kolb, S. Paramonov, T. Guns and L. De Raedt. Learning constraints in spreadsheets and tabular data. 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Logic of generality. In Encyclopedia of Machine Learning and Data Mining, pages 772-780, 2017 [ BibTeX | DiVA ] |
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T. Guns, A. Dries, S. Nijssen, G. Tack and L. De Raedt. MiningZinc : A declarative framework for constraint-based mining. Artificial Intelligence, 244:6-29, 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Multi-relational Data Mining. In Encyclopedia of machine learning and data mining, pages 892-893, 2017 [ BibTeX | DiVA ] |
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D. Nitti, V. Belle, T. Laet and L. De Raedt. Planning in hybrid relational MDPs. Machine Learning, 106(12):1905-1932, 2017 [ BibTeX | DiVA ] |
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A. Kimming and L. De Raedt. Probabilistic Logic Programs : Unifying Program Trace and Possible World Semantics. 2017 [ BibTeX | DiVA ] |
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L. De Raedt. Probabilistic Programming and its Applications. In KI 2017: Advances in Artificial Intelligence : 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings 2017 [ BibTeX | DiVA ] |
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S. Paramonov, M. van Leeuwen and L. De Raedt. Relational data factorization. Machine Learning, 106(12):1867-1904, 2017 [ BibTeX | DiVA ] |
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T. Le Van, S. Nijssen, M. van Leeuwen and L. De Raedt. Semiring Rank Matrix Factorization. IEEE Transactions on Knowledge and Data Engineering, 29(8):1737-1750, 2017 [ BibTeX | DiVA ] |
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A. Dries, J. Davis, V. Belle and L. De Raedt. Solving Probability Problems in Natural Language. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 3981-3987, 2017 [ BibTeX | DiVA ] |
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L. De Raedt and K. Kersting. Statistical relational learning. In Encyclopedia of Machine Learning and Data Mining, pages 772-780, 2017 [ BibTeX | DiVA ] |
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B. Babaki, T. Guns and L. De Raedt. Stochastic Constraint Programming with And-Or Branch-and-Bound. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 539-545, 2017 [ BibTeX | DiVA ] |
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S. Paramonov, S. Kolb, T. Guns and L. De Raedt. TaCLe : Learning Constraints in Tabular Data. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2511-2514, 2017 [ BibTeX | DiVA ] |
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C. Bessiere, L. De Raedt, T. Guns, L. Kotthoff, M. Nanni, S. Nijssen, B. O’Sullivan, A. Paparrizou, D. Pedreschi and H. Simonis. The Inductive Constraint Programming Loop. IEEE Intelligent Systems, 32(5):44-52, 2017 [ BibTeX | DiVA ] |
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G. Verbruggen and L. De Raedt. Towards automated relational data wrangling. In Proceedings of AutoML2017 @ ECML-PKDD: Automatic selection, configuration and composition of machine learning algorithms, 1998:18-26, 2017 [ BibTeX | DiVA ] |
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S. Paramonov, M. van Leuween, M. Denecker and L. De Raedt. An Exercise in Declarative Modeling for Relational Query Mining. In Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers, 9575(9575):166-182, 2016 [ BibTeX | DiVA ] |
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L. De Raedt. An Introduction to Hybrid Probabilistic (Logic) Programming. 2016 [ BibTeX | DiVA ] |
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L. De Raedt. Can we automate data science?. In European Data Science Conference : November 07-08, 2016 in Luxembourg, pages 38-38, 2016 [ BibTeX | DiVA ] |
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L. De Raedt, Y. Deville, M. Bui and D. L. Truong. Editors' Introduction to the Special Issue on : The Sixth International Symposium on Information and Communication Technology - SoICT 2015. Informatica, 40(2):157-157, 2016 [ BibTeX | DiVA ] |
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J. Vlasselaer, W. Meert, G. Van den Broeck and L. De Raedt. Exploiting local and repeated structure in Dynamic Bayesian Networks. Artificial Intelligence, 232:43-53, 2016 [ BibTeX | DiVA ] |
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J. Vlasselaer, A. Kimmig, A. Dries, W. Meert and L. De Raedt. Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. In Proceedings of the First Workshop on Beyond NP, pages 359-364, 2016 [ BibTeX | DiVA ] |
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F. Orsini, P. Frasconi and L. De Raedt. kProbLog : An Algebraic Prolog for Kernel Programming. In Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers, 9575(9575):152-165, 2016 [ BibTeX | DiVA ] |
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L. De Raedt, A. Dries, T. Guns and C. Bessiere. Learning Constraint Satisfaction Problems : an ILP Perspective. In Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach, pages 96-112, 2016 [ BibTeX | DiVA ] |
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D. Nitti, I. Ravkic, J. Davis and L. De Raedt. Learning the structure of dynamic hybrid relational models. In ECAI 2016 : Proceedings, 285(285):1283-1290, 2016 [ BibTeX | DiVA ] |
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A. Dries, T. Guns, S. Nijssen, B. Babaki, T. Le Van, B. Negrevergne, S. Paramonov and L. De Raedt. Modeling in MiningZinc. In Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach, pages 257-281, 2016 [ BibTeX | DiVA ] |
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D. De Maeyer, B. Weytjens, L. De Raedt and K. Marchal. Network-Based Analysis of eQTL Data to Prioritize Driver Mutations. Genome Biology and Evolution, 23;8(3):481-494, 2016 [ BibTeX | DiVA ] |
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L. De Raedt. On the history and future of machine learning : A personal interpretation and perspective. 2016 [ BibTeX | DiVA ] |
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D. Nitti, T. De Laet and L. De Raedt. Probabilistic logic programming for hybrid relational domains. Machine Learning, 103(3):407-449, 2016 [ BibTeX | DiVA ] |
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L. De Raedt. Probabilistic Programs and Their Applications. In 4th Conference of SANKEN Core to Core Program : Proceedings 2016 [ BibTeX | DiVA ] |
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V. Vercruyssen, L. De Raedt and J. Davis. Qualitative spatial reasoning for soccer pass prediction. In 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) 2016 [ BibTeX | DiVA ] |
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L. Antanas, P. Moreno and L. De Raedt. Relational Kernel-Based Grasping with Numerical Features. In Inductive Logic Programming : 25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers, 9575(9575):1-14, 2016 [ BibTeX | DiVA ] |
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T. Le Van, M. van Leeuwen, A. C. Fierro, D. De Maeyer, J. Van den Eynden, L. Verbeke, L. De Raedt, K. Marchal and S. Nijssen. Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics, 32(17):445-454, 2016 [ BibTeX | DiVA ] |
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L. De Raedt, K. Kersting, S. Natarajan and D. Poole. Statistical Relational Artificial Intelligence : Logic, Probability, and Computation. , pages 189, 2016 [ BibTeX | DiVA ] |
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C. Bessiere, L. De Raedt, T. Guns, L. Kotthoff, M. Nanni, S. Nijssen, B. O’Sullivan, A. Paparrizou, D. Pedreschi and H. Simonis. The Inductive Constraint Programming Loop. In Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach, pages 303-309, 2016 [ BibTeX | DiVA ] |
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L. De Raedt. Towards synthesising inductive data models. 2016 [ BibTeX | DiVA ] |
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M. Kumar, S. Teso, P. De Causmaecker and L. De Raedt. Automating Personnel Rostering by Learning Constraints Using Tensors. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, pages 697-704, 2019 [ BibTeX | DiVA ] |
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J. Vlasselae, G. Van den Broeck, A. Kimmig, W. Meert and L. De Raedt. Anytime Inference in Probabilistic Logic Programs with TP-Compilation. In Proceedings of 24th International Joint Conference on ArtificialIntelligence (IJCAI), pages 1852-1858, 2015 [ BibTeX | DiVA ] |
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B. Babaki, T. Guns, S. Nijssen and L. De Raedt. Constraint-Based Querying for Bayesian Network Exploration. In Advances in Intelligent Data Analysis XIV : 14th International Symposium, IDA 2015, Saint Etienne, France, October 22 -24, 2015. Proceedings, 9385(9385):13-24, 2015 [ BibTeX | DiVA ] |
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L. De Raedt. Declarative Machine Learning and Data Mining. In Constraint programming for Analytics Workshop 2015 [ BibTeX | DiVA ] |
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L. De Raedt, Y. Deville, M. Bui, D. L. Truong, T. H. Quyet and A. P. Le. Foreword. In Proceedings of the Sixth International Symposium on Information and Communication Technology, pages v-v, 2015 [ BibTeX | DiVA ] |
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F. Orsini, P. Frasconi and L. De Raedt. Graph Invariant Kernels. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pages 3756-3762, 2015 [ BibTeX | DiVA ] |
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L. De Raedt, A. Dries, I. Thon, G. Van den Broeck and M. Verbeke. Inducing Probabilistic Relational Rules from Probabilistic Examples. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pages 1835-1842, 2015 [ BibTeX | DiVA ] |
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D. Fierens, G. Vam Den Broeck, J. Renkens, D. Shterionov, B. Gutmann, I. Thon, G. Janssens and L. De Raedt. Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming, 15(3):358-401, 2015 [ BibTeX | DiVA ] |
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J. Cussens, L. De Raedt, A. Kimmig and T. Sato. Introduction to the special issue on probability, logic and learning. Theory and Practice of Logic Programming, 15(2):145-146, 2015 [ BibTeX | DiVA ] |
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P. Frasconi, F. Costa, L. De Raedt and K. De Grave. kLog : A language for logical and relational learning with kernels. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), pages 4183-4187, 2015 [ BibTeX | DiVA ] |
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L. De Raedt. Languages for Learning and Mining. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 6:4107-4111, 2015 [ BibTeX | DiVA ] |
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A. d'Avila Garcez, T. R. Besold, L. De Raedt, P. Földiák, P. Hitzler, T. Icard, K. U. Kiihnberger, L. C. Lamb, R. Miikkulainen and D. L. Silver. 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, SS-15-03:18-21, 2015 [ BibTeX | DiVA ] |
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D. Van Daele, A. Kimmig and L. De Raedt. PageRank, ProPPR, and Stochastic Logic Programs. In Inductive Logic Programming : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers, 9046(9046):168-180, 2015 [ BibTeX | DiVA ] |
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D. De Maeyer, B. Weytjens, J. Renkens, L. De Raedt and K. Marchal. PheNetic : network-based interpretation of molecular profiling data. Nucleic Acids Research, 43(W1):244-250, 2015 [ BibTeX | DiVA ] |
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D. Nitti, V. Belle and L. De Raedt. Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. In Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II, pages 327-342, 2015 [ BibTeX | DiVA ] |
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L. De Raedt and A. Kimmig. Probabilistic (logic) programming concepts. Machine Learning, 100(1):5-47, 2015 [ BibTeX | DiVA ] |
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L. De Raedt. Probabilistic programming and its applications (Keynote Abstract). In Multi-disciplinary Trends in Artificial Intelligence : 9th International Workshop, MIWAI 2015, Fuzhou, China, November 13-15, 2015, Proceedings, 9426(9426):xiii-xiv, 2015 [ BibTeX | DiVA ] |
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A. Dries, A. Kimmig, W. Meert, J. Renkens, G. Van den Broeck, J. Vlasselaer and L. De Raedt. ProbLog2 : Probabilistic Logic Programming. In Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III, 9286(9286):312-315, 2015 [ BibTeX | DiVA ] |
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T. Le Van, M. van Leuween, S. Nijssen and L. De Raedt. Rank Matrix Factorisation. In 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, 9077(9077):734-746, 2015 [ BibTeX | DiVA ] |
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L. De Raedt. Towards probabilistic inductive programming synthesis. In Approaches and Applications ofInductive Programming 2015 [ BibTeX | DiVA ] |
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L. De Raedt. Using and developing declarative languages for machine learning and data mining. In Technical Communications of ICLP : Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015) 2015 [ BibTeX | DiVA ] |
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J. Vlasselaer, W. Meert, G. Van den Broeck and L. De Raedt. AAAI Workshop - Technical Report. In Papers from the 2014 AAAI Workshop, WS-14-13(2014; Vol. WS-14-13):131-134, 2014 [ BibTeX | DiVA ] |
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D. Nitti, G. Chliveros, M. Pateraki, L. De Raedt, E. Hourdakis and P. Trahanias. Application of Dynamic Distributional Clauses for Multi-hypothesis Initialization in Model-based Object Tracking. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - (Volume 1), 1:256-261, 2014 [ BibTeX | DiVA ] |
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J. Vlasselaer, J. Renkens, G. Van den Broeck and L. De Raedt. Compiling Probabilistic Logic Programs into Sentential Decision Diagrams. In Workshop on Probabilistic Logic Programming (PLP) : Proceedings, 3:1-10, 2014 [ BibTeX | DiVA ] |
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J. Vlasselaer, W. Meert, R. Langone and L. De Raedt. Condition monitoring with incomplete observations. In ECAI 2014: 21st European Conference on Artificial Intelligence 18-22 August 2014, Prague, Czech Republic : Proceedings, 263(263):1215-1216, 2014 [ BibTeX | DiVA ] |
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D. Nitti, T. De Lact and L. De Raedt. Distributional Clauses Particle Filter. In Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III, 8726(8726):504-507, 2014 [ BibTeX | DiVA ] |
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J. Renkens, A. Kimmig, G. Van den Broeck and L. De Raedt. Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, 4:2490-2496, 2014 [ BibTeX | DiVA ] |
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V. Dzyuba, M. van Leeuwen, S. Nijssen and L. De Raedt. Interactive Learning of Pattern Rankings. International journal on artificial intelligence tools, 23(6), 2014 [ BibTeX | DiVA ] |
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M. Fox and L. De Raedt. Introduction to the Special Issue on the ECAI 2012 Turing and Anniversary Track. AI Communications, 27(1):1-1, 2014 [ BibTeX | DiVA ] |
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P. Frasconi, F. Costa, L. De Raedt and K. De Grave. kLog : A language for logical and relational learning with kernels. Artificial Intelligence, 217:117-143, 2014 [ BibTeX | DiVA ] |
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M. Verbeke, P. Frasconi, K. De Grave, F. Costa and L. De Raedt. kLogNLP : Graph Kernel–based Relational Learning of Natural Language. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics : System Demonstrations, pages 85-90, 2014 [ BibTeX | DiVA ] |
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M. Verbeke, V. Van Asch, W. Daelemans and L. De Raedt. Lazy and Eager Relational Learning Using Graph-Kernels. In Statistical Language and Speech Processing : Second International Conference, SLSP 2014, Grenoble, France, October 14-16, 2014, Proceedings, pages 171-184, 2014 [ BibTeX | DiVA ] |
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L. De Raedt, A. Dries, T. Guns and C. Bessiere. Learning constraint satisfaction problems : An ILP perspective. , 24:1-6, 2014 [ BibTeX | DiVA ] |
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B. Moldovan and L. De Raedt. Learning relational affordance models for two-arm robots. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2916-2922, 2014 [ BibTeX | DiVA ] |
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B. Moldovan and L. De Raedt. Occluded object search by relational affordances. In 2014 IEEE International Conference on Robotics & Automation (ICRA), pages 169-174, 2014 [ BibTeX | DiVA ] |
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D. Van Daele, A. Kimmig and L. De Raedt. PageRank, ProPPR, and Stochastic Logic Programs. In Inductive Logic Programming : 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers, 9046(9046):168-180, 2014 [ BibTeX | DiVA ] |
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T. Le Van, M. van Leuween, S. Nijssen, A. C. Fierro, K. Marchal and L. De Raedt. Ranked Tiling. In Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II, pages 98-113, 2014 [ BibTeX | DiVA ] |
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D. Nitti, T. De Laet and L. De Raedt. Relational object tracking and learning. In 2014 IEEE International Conference on Robotics and Automation (ICRA) 2014 [ BibTeX | DiVA ] |
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F. Costa, M. Verbeke and L. De Raedt. Relational Regularization and Feature Ranking. In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), 2:650-658, 2014 [ BibTeX | DiVA ] |
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B. Moldovan, M. van Otterlo, L. De Raedt, P. Moreno and J. Santos-Victor. Statistical Relational Learning of Object Affordances for Robotic Manipulation. In Latest Advances in Inductive Logic Programming, pages 95-103, 2014 [ BibTeX | DiVA ] |
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L. Antanas, M. Van Otterlo, J. Oramas Mogrovejo, T. Tuytelaars and L. De Raedt. There are plenty of places like home : Using relational representations in hierarchies for distance-based image understanding. Neurocomputing, 123:75-85, 2014 [ BibTeX | DiVA ] |
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J. M. Oramas, L. De Raedt and T. Tuytelaars. Towards Cautious Collective Inference for Object Verification. In IEEE Workshop on Applications of Computer Vision (WACV), pages 269-276, 2014 [ BibTeX | DiVA ] |
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M. Theobald, L. De Raedt, M. Dylla, A. Kimmig and I. Miliaraki. 10 Years of Probabilistic Querying : What Next?. In Advances in Databases and Information Systems : 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013. Proceedings, 8133(8133):1-13, 2013 [ BibTeX | DiVA ] |
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D. Nitti, T. De Laet and L. De Raedt. A particle filter for hybrid relational domains. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2764-2771, 2013 [ BibTeX | DiVA ] |
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L. Antanas, M. Hoffmann, P. Frasconi, T. Tuytelaars and L. De Raedt. A relational kernel-based approach to scene classification. In Proceedings of IEEE Workshop on Applications of Computer Vision, pages 133-139, 2013 [ BibTeX | DiVA ] |
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V. Dzyuba, M. van Leuween, S. Nijssen and L. De Raedt. Active Preference Learning for Ranking Patterns. In 25th International Conference on Tools with Artificial Intelligence ICTA I2013 : Proceedings, pages 532-539, 2013 [ BibTeX | DiVA ] |
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J. M. Oramas, L. De Raedt and T. Tuytelaars. Allocentric Pose Estimation. In 2013 IEEE International Conference on Computer Vision, pages 289-296, 2013 [ BibTeX | DiVA ] |
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B. Moldovan, I. Thon, J. Davis and L. De Raedt. Estimation of Conditional Probabilities in Probabilistic Programming Languages. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 12th European Conference, ECSQARU 2013, Utrecht, The Netherlands, July 8-10, 2013. Proceedings, 7958(7958):436-448, 2013 [ BibTeX | DiVA ] |
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T. Guns, S. Nijssen and L. De Raedt. k-Pattern Set Mining under Constraints. IEEE Transactions on Knowledge and Data Engineering, 25(2):402-418, 2013 [ BibTeX | DiVA ] |
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G. C. Garriga, R. Khardon and L. De Raedt. Mining closed patterns in relational, graph and network data. Annals of Mathematics and Artificial Intelligence, 69(4):315-342, 2013 [ BibTeX | DiVA ] |
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T. Guns, G. Tack, S. Nijssen and L. De Raedt. MiningZinc : A Modeling Language for Constraint-based Mining. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pages 1365-1372, 2013 [ BibTeX | DiVA ] |
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D. De Maeyer, J. Renkens, L. Cloots, L. De Raedt and K. Marchal. PheNetic : Network-based interpretation of unstructured gene lists in E. coli. Molecular Biosystems, 9(7):1594-1603, 2013 [ BibTeX | DiVA ] |
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L. De Raedt, S. Paramono and M. van Leeuwen. Relational Decomposition using Answer Set Programming. 2013 [ BibTeX | DiVA ] |
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L. De Raedt, S. Paramono and M. van Leeuwen. Relational Decomposition using Answer Set Programming. In Online Preprints 23rd International Conference on Inductive Logic Programming 2013 [ BibTeX | DiVA ] |
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T. Guns, A. Dries, G. Tack, S. Nijssen and L. De Raedt. The MiningZinc Framework for Constraint-Based Itemset Mining. In 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pages 1081-1084, 2013 [ BibTeX | DiVA ] |
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P. Zuidberg Dos Martires, A. Dries and L. De Raedt. Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation. In Proceedings of the AAAI Conference on Artificial Intelligence, 33:1(33:1):7825-7833, 2019 [ BibTeX | DiVA ] |
