Luc De Raedt

Luc De Raedt

Wallenberg Guest Professor

AASS Research Center
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T2252a
Phone +46 (0)19 30 30 11
luc.de-raedt@oru.se

CVGoogle Scholar

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


Publications

[1] A. Persson, P. Zuidberg Dos Martires, A. Loutfi and L. De Raedt. Semantic Relational Object Tracking.BibTeX | DiVA ]
[2] 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 ]
[3] 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, 2020BibTeX | DiVA ]
[4] 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, 2020BibTeX | DiVA ]
[5] S. Kolb, S. Teso, A. Dries and L. De Raedt. Predictive spreadsheet autocompletion with constraints. Machine Learning, 109(2):307-325, 2020BibTeX | DiVA ]
[6] A. Persson, P. Zuidberg Dos Martires, A. Loutfi and L. De Raedt. Semantic Relational Object Tracking. IEEE Transactions on Cognitive and Developmental Systems, 12(1):84-97, 2020BibTeX | DiVA ]
[7] V. Belle and L. De Raedt. Semiring programming : A semantic framework for generalized sum product problems. International Journal of Approximate Reasoning, 126:181-201, 2020BibTeX | DiVA ]
[8] 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, 2020BibTeX | DiVA ]
[9] 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, 2021BibTeX | DiVA ]
[10] A. Persson, P. Z. D. Martires, L. De Raedt and A. Loutfi. ProbAnch : a Modular Probabilistic Anchoring Framework. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 5285-5287, 2021BibTeX | DiVA ]
[11] 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, 2019BibTeX | DiVA ]
[12] 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, 2019BibTeX | DiVA ]
[13] 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, 2019BibTeX | DiVA ]
[14] 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, 2019BibTeX | DiVA ]
[15] G. Verbruggen and L. De Raedt. Automatically Wrangling Spreadsheets into Machine Learning Data Formats. In Advances in Intelligent Data Analysis XVII, pages 367-379, 2018BibTeX | DiVA ]
[16] M. Kumar, S. Teso, P. De Causmaecker and L. De Raedt. Automating Personnel Rostering by Learning Constraints Using Tensors. 2018BibTeX | DiVA ]
[17] 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, 2018BibTeX | DiVA ]
[18] 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), 2018BibTeX | DiVA ]
[19] 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, 2018BibTeX | DiVA ]
[20] 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, 2018BibTeX | DiVA ]
[21] 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, 2018BibTeX | DiVA ]
[22] 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, 2018BibTeX | DiVA ]
[23] 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, 2018BibTeX | DiVA ]
[24] A. Kimmig, G. Van den Broeck and L. De Raedt. Algebraic Model Counting. Journal of Applied Logic, 22:46-62, 2017BibTeX | DiVA ]
[25] 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, 2017BibTeX | DiVA ]
[26] 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, 2017BibTeX | DiVA ]
[27] 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, 2017BibTeX | DiVA ]
[28] L. De Raedt. Inductive Logic Programming. In Encyclopedia of machine learning and data mining 2017BibTeX | DiVA ]
[29] F. Orsini, P. Frasconi and L. De Raedt. kProbLog : an algebraic Prolog for machine learning. Machine Learning, pages 1933-1969, 2017BibTeX | DiVA ]
[30] S. Kolb, S. Paramonov, T. Guns and L. De Raedt. Learning constraints in spreadsheets and tabular data. Machine Learning, pages 1441-1468, 2017BibTeX | DiVA ]
[31] L. De Raedt. Logic of generality. In Encyclopedia of Machine Learning and Data Mining, pages 772-780, 2017BibTeX | DiVA ]
[32] 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, 2017BibTeX | DiVA ]
[33] L. De Raedt. Multi-relational Data Mining. In Encyclopedia of machine learning and data mining, pages 892-893, 2017BibTeX | DiVA ]
[34] D. Nitti, V. Belle, T. Laet and L. De Raedt. Planning in hybrid relational MDPs. Machine Learning, 106(12):1905-1932, 2017BibTeX | DiVA ]
[35] S. Paramonov, M. van Leeuwen and L. De Raedt. Relational data factorization. Machine Learning, 106(12):1867-1904, 2017BibTeX | DiVA ]
[36] 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, 2017BibTeX | DiVA ]
[37] 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, 2017BibTeX | DiVA ]
[38] L. De Raedt and K. Kersting. Statistical relational learning. In Encyclopedia of Machine Learning and Data Mining, pages 772-780, 2017BibTeX | DiVA ]
[39] 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, 2017BibTeX | DiVA ]
[40] 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, 2017BibTeX | DiVA ]
[41] 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, 2017BibTeX | DiVA ]
[42] 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, 2017BibTeX | DiVA ]
[43] 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, 2016BibTeX | DiVA ]
[44] 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, 2016BibTeX | DiVA ]
[45] 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, 2016BibTeX | DiVA ]
[46] 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, 2016BibTeX | DiVA ]
[47] 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, 2016BibTeX | DiVA ]
[48] 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, 2016BibTeX | DiVA ]
[49] 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, 2016BibTeX | DiVA ]
[50] 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, 2016BibTeX | DiVA ]
[51] 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, 2016BibTeX | DiVA ]
[52] D. Nitti, T. De Laet and L. De Raedt. Probabilistic logic programming for hybrid relational domains. Machine Learning, 103(3):407-449, 2016BibTeX | DiVA ]
[53] 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) 2016BibTeX | DiVA ]
[54] 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, 2016BibTeX | DiVA ]
[55] 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, 2016BibTeX | DiVA ]
[56] L. De Raedt, K. Kersting, S. Natarajan and D. Poole. Statistical Relational Artificial Intelligence : Logic, Probability, and Computation. , pages 189, 2016BibTeX | DiVA ]
[57] 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, 2016BibTeX | DiVA ]
[58] 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, 2019BibTeX | DiVA ]
[59] 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, 2015BibTeX | DiVA ]
[60] 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, 2015BibTeX | DiVA ]
[61] 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, 2015BibTeX | DiVA ]
[62] 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, 2015BibTeX | DiVA ]
[63] 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, 2015BibTeX | DiVA ]
[64] 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, 2015BibTeX | DiVA ]
[65] 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, 2015BibTeX | DiVA ]
[66] 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, 2015BibTeX | DiVA ]
[67] L. De Raedt. Languages for Learning and Mining. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 6:4107-4111, 2015BibTeX | DiVA ]
[68] 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, 2015BibTeX | DiVA ]
[69] 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, 2015BibTeX | DiVA ]
[70] 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, 2015BibTeX | DiVA ]
[71] 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, 2015BibTeX | DiVA ]
[72] L. De Raedt and A. Kimmig. Probabilistic (logic) programming concepts. Machine Learning, 100(1):5-47, 2015BibTeX | DiVA ]
[73] 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, 2015BibTeX | DiVA ]
[74] 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, 2015BibTeX | DiVA ]
[75] 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, 2015BibTeX | DiVA ]
[76] 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, 2014BibTeX | DiVA ]
[77] 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, 2014BibTeX | DiVA ]
[78] 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, 2014BibTeX | DiVA ]
[79] 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, 2014BibTeX | DiVA ]
[80] 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, 2014BibTeX | DiVA ]
[81] 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, 2014BibTeX | DiVA ]
[82] V. Dzyuba, M. van Leeuwen, S. Nijssen and L. De Raedt. Interactive Learning of Pattern Rankings. International journal on artificial intelligence tools, 23(6), 2014BibTeX | DiVA ]
[83] 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, 2014BibTeX | DiVA ]
[84] 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, 2014BibTeX | DiVA ]
[85] 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, 2014BibTeX | DiVA ]
[86] 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, 2014BibTeX | DiVA ]
[87] L. De Raedt, A. Dries, T. Guns and C. Bessiere. Learning constraint satisfaction problems : An ILP perspective. , 24:1-6, 2014BibTeX | DiVA ]
[88] 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, 2014BibTeX | DiVA ]
[89] B. Moldovan and L. De Raedt. Occluded object search by relational affordances. In 2014 IEEE International Conference on Robotics & Automation (ICRA), pages 169-174, 2014BibTeX | DiVA ]
[90] 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, 2014BibTeX | DiVA ]
[91] 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, 2014BibTeX | DiVA ]
[92] D. Nitti, T. De Laet and L. De Raedt. Relational object tracking and learning. In 2014 IEEE International Conference on Robotics and Automation (ICRA) 2014BibTeX | DiVA ]
[93] 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, 2014BibTeX | DiVA ]
[94] 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, 2014BibTeX | DiVA ]
[95] 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, 2014BibTeX | DiVA ]
[96] 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, 2014BibTeX | DiVA ]
[97] 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, 2013BibTeX | DiVA ]
[98] 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, 2013BibTeX | DiVA ]
[99] 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, 2013BibTeX | DiVA ]
[100] 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, 2013BibTeX | DiVA ]
[101] J. M. Oramas, L. De Raedt and T. Tuytelaars. Allocentric Pose Estimation. In 2013 IEEE International Conference on Computer Vision, pages 289-296, 2013BibTeX | DiVA ]
[102] 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, 2013BibTeX | DiVA ]
[103] 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, 2013BibTeX | DiVA ]
[104] 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, 2013BibTeX | DiVA ]
[105] 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, 2013BibTeX | DiVA ]
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