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ORCIDLuc De Raedt
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- affiliation: Catholic University of Leuven, Belgium
- affiliation: University of Freiburg, Germany
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2020 – today
- 2022
[j83]Dries Van Daele, Bram Weytjens, Luc De Raedt, Kathleen Marchal:
OMEN: network-based driver gene identification using mutual exclusivity. Bioinform. 38(12): 3245-3251 (2022)- [j82]Tijl De Bie, Luc De Raedt
, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating data science. Commun. ACM 65(3): 76-87 (2022) - [j81]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. 22(1): 81-114 (2022) - [c202]Wen-Chi Yang, Arcchit Jain, Luc De Raedt, Wannes Meert:
Parameter Learning in ProbLog with Annotated Disjunctions. IDA 2022: 378-391 - [i52]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. CoRR abs/2201.11165 (2022) - [i51]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. CoRR abs/2202.03888 (2022) - 2021
- [j80]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
Neural probabilistic logic programming in DeepProbLog. Artif. Intell. 298: 103504 (2021) - [c201]Mohit Kumar, Samuel Kolb, Clément Gautrais, Luc De Raedt:
Democratizing Constraint Satisfaction Problems through Machine Learning. AAAI 2021: 16057-16059 - [c200]Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc De Raedt, Walter Daelemans:
Mapping probability word problems to executable representations. EMNLP (1) 2021: 3627-3640 - [c199]Gillis Hermans, Thomas Winters, Luc De Raedt:
Shape Inference and Grammar Induction for Example-Based Procedural Generation. ICCC 2021: 342-349 - [c198]Gust Verbruggen, Elia Van Wolputte, Sebastijan Dumancic, Luc De Raedt:
avatar - Automated Feature Wrangling for Machine Learning. IDA 2021: 235-247 - [c197]Gust Verbruggen, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Luc De Raedt:
Muppets: Multipurpose Table Segmentation. IDA 2021: 389-401 - [c196]Dirko Coetsee, Steve Kroon, McElory Hoffmann, Luc De Raedt:
SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints. IDA 2021: 402-413 - [c195]Arcchit Jain, Clément Gautrais, Angelika Kimmig, Luc De Raedt:
Learning CNF Theories Using MDL and Predicate Invention. IJCAI 2021: 2599-2605 - [c194]Robin Manhaeve, Giuseppe Marra, Luc De Raedt:
Approximate Inference for Neural Probabilistic Logic Programming. KR 2021: 475-486 - [p16]Robin Manhaeve, Giuseppe Marra, Thomas Demeester, Sebastijan Dumancic, Angelika Kimmig, Luc De Raedt:
Neuro-Symbolic AI = Neural + Logical + Probabilistic AI. Neuro-Symbolic Artificial Intelligence 2021: 173-191 - [i50]Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis:
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer. CoRR abs/2104.03252 (2021) - [i49]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating Data Science: Prospects and Challenges. CoRR abs/2105.05699 (2021) - [i48]Wen-Chi Yang, Jean-François Raskin, Luc De Raedt:
Lifted Model Checking for Relational MDPs. CoRR abs/2106.11735 (2021) - [i47]Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt:
DeepStochLog: Neural Stochastic Logic Programming. CoRR abs/2106.12574 (2021) - [i46]Mohit Kumar, Samuel Kolb, Luc De Raedt, Stefano Teso:
Learning Mixed-Integer Linear Programs from Contextual Examples. CoRR abs/2107.07136 (2021) - [i45]Giuseppe Marra, Sebastijan Dumancic, Robin Manhaeve, Luc De Raedt:
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey. CoRR abs/2108.11451 (2021) - [i44]Gillis Hermans, Thomas Winters, Luc De Raedt:
Shape Inference and Grammar Induction for Example-based Procedural Generation. CoRR abs/2109.10217 (2021) - [i43]Simon Vandevelde, Victor Verreet, Luc De Raedt, Joost Vennekens:
A Table-Based Representation for Probabilistic Logic: Preliminary Results. CoRR abs/2110.01909 (2021) - [i42]Pietro Totis, Angelika Kimmig, Luc De Raedt:
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation. CoRR abs/2110.01990 (2021) - [i41]Andrew Cropper, Luc De Raedt, Richard Evans, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192). Dagstuhl Reports 11(4): 20-33 (2021) - 2020
- [j79]Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, Luc De Raedt:
Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring. Frontiers Robotics AI 7: 100 (2020) - [j78]Vaishak Belle, Luc De Raedt:
Semiring programming: A semantic framework for generalized sum product problems. Int. J. Approx. Reason. 126: 181-201 (2020) - [j77]Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt:
Predictive spreadsheet autocompletion with constraints. Mach. Learn. 109(2): 307-325 (2020) - [j76]Andreas Persson, Pedro Zuidberg Dos Martires, Luc De Raedt, Amy Loutfi:
Semantic Relational Object Tracking. IEEE Trans. Cogn. Dev. Syst. 12(1): 84-97 (2020) - [c193]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. AAAI 2020: 4493-4500 - [c192]Vincent Derkinderen, Luc De Raedt:
Algebraic Circuits for Decision Theoretic Inference and Learning. ECAI 2020: 2569-2576 - [c191]Thomas Winters, Luc De Raedt:
Discovering Textual Structures: Generative Grammar Induction using Template Trees. ICCC 2020: 177-180 - [c190]Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, Giuseppe Marra:
From Statistical Relational to Neuro-Symbolic Artificial Intelligence. IJCAI 2020: 4943-4950 - [c189]Andreas Persson, Pedro Zuidberg Dos Martires, Luc De Raedt, Amy Loutfi:
ProbAnch: a Modular Probabilistic Anchoring Framework. IJCAI 2020: 5285-5287 - [c188]Clément Gautrais, Yann Dauxais, Samuel Kolb, Arcchit Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
VisualSynth: Democratizing Data Science in Spreadsheets. ECML/PKDD (5) 2020: 550-554 - [c187]Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc De Raedt:
Ordering Variables for Weighted Model Integration. UAI 2020: 879-888 - [i40]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
Learning Distributional Programs for Relational Autocompletion. CoRR abs/2001.08603 (2020) - [i39]Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, Luc De Raedt:
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring. CoRR abs/2002.10373 (2020) - [i38]Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, Giuseppe Marra:
From Statistical Relational to Neuro-Symbolic Artificial Intelligence. CoRR abs/2003.08316 (2020) - [i37]Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt:
Human-Machine Collaboration for Democratizing Data Science. CoRR abs/2004.11113 (2020) - [i36]Thomas Winters, Luc De Raedt:
Discovering Textual Structures: Generative Grammar Induction using Template Trees. CoRR abs/2009.04530 (2020)
2010 – 2019
- 2019
- [j75]Laura Antanas, Plinio Moreno, Marion Neumann, Rui Pimentel de Figueiredo, Kristian Kersting, José Santos-Victor, Luc De Raedt:
Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. Auton. Robots 43(6): 1393-1418 (2019) - [c186]Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt:
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation. AAAI 2019: 7825-7833 - [c185]Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt:
Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC 2019 - [c184]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. BNAIC/BENELEARN 2019 - [c183]Mohit Kumar, Stefano Teso, Patrick De Causmaecker, Luc De Raedt:
Automating Personnel Rostering by Learning Constraints Using Tensors. ICTAI 2019: 697-704 - [c182]Mohit Kumar, Stefano Teso, Luc De Raedt:
Acquiring Integer Programs from Data. IJCAI 2019: 1130-1136 - [c181]Samuel Kolb, Paolo Morettin, Pedro Zuidberg Dos Martires, Francesco Sommavilla, Andrea Passerini, Roberto Sebastiani, Luc De Raedt:
The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration. IJCAI 2019: 6530-6532 - [c180]Yann Dauxais, Clément Gautrais, Anton Dries, Arcchit Jain, Samuel Kolb, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract). PKDD/ECML Workshops (1) 2019: 102-110 - [c179]Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt:
How to Exploit Structure while Solving Weighted Model Integration Problems. UAI 2019: 744-754 - [i35]Andreas Persson, Pedro Zuidberg Dos Martires, Amy Loutfi, Luc De Raedt:
Semantic Relational Object Tracking. CoRR abs/1902.09937 (2019) - [i34]Ozan Arkan Can, Pedro Zuidberg Dos Martires, Andreas Persson, Julian Gaal, Amy Loutfi, Luc De Raedt, Deniz Yuret, Alessandro Saffiotti:
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations. CoRR abs/1904.13324 (2019) - [i33]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. CoRR abs/1907.08194 (2019) - [i32]Luc De Raedt, Richard Evans, Stephen H. Muggleton, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). Dagstuhl Reports 9(5): 58-88 (2019) - 2018
- [j74]Bogdan Moldovan, Plinio Moreno, Davide Nitti, José Santos-Victor, Luc De Raedt:
Relational affordances for multiple-object manipulation. Auton. Robots 42(1): 19-44 (2018) - [c178]Luc De Raedt, Andrea Passerini, Stefano Teso:
Learning Constraints From Examples. AAAI 2018: 7965-7970 - [c177]Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt:
Sketched Answer Set Programming. ICTAI 2018: 694-701 - [c176]Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen:
Elements of an Automatic Data Scientist. IDA 2018: 3-14 - [c175]Gust Verbruggen, Luc De Raedt:
Automatically Wrangling Spreadsheets into Machine Learning Data Formats. IDA 2018: 367-379 - [c174]Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt:
Learning SMT(LRA) Constraints using SMT Solvers. IJCAI 2018: 2333-2340 - [c173]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. NeurIPS 2018: 3753-3763 - [i31]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. CoRR abs/1805.10872 (2018) - [i30]Mohit Kumar, Stefano Teso, Luc De Raedt:
Automating Personnel Rostering by Learning Constraints Using Tensors. CoRR abs/1805.11375 (2018) - [i29]Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt:
Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming. CoRR abs/1807.00614 (2018) - [i28]Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth:
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8(9): 154-181 (2018) - 2017
- [j73]Tias Guns, Anton Dries, Siegfried Nijssen, Guido Tack, Luc De Raedt:
MiningZinc: A declarative framework for constraint-based mining. Artif. Intell. 244: 6-29 (2017) - [j72]José Oramas M., Luc De Raedt, Tinne Tuytelaars:
Context-based object viewpoint estimation: A 2D relational approach. Comput. Vis. Image Underst. 160: 100-113 (2017) - [j71]Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt:
Flexible constrained sampling with guarantees for pattern mining. Data Min. Knowl. Discov. 31(5): 1266-1293 (2017) - [j70]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. IEEE Intell. Syst. 32(5): 44-52 (2017) - [j69]Luc De Raedt, Marc Bui, Yves Deville, Dieu Linh Truong:
Editors' Introduction to the Special Issue on "Information and Communication Technology". Informatica (Slovenia) 41(2) (2017) - [j68]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic model counting. J. Appl. Log. 22: 46-62 (2017) - [j67]Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt:
Learning constraints in spreadsheets and tabular data. Mach. Learn. 106(9-10): 1441-1468 (2017) - [j66]Sergey Paramonov, Matthijs van Leeuwen, Luc De Raedt:
Relational data factorization. Mach. Learn. 106(12): 1867-1904 (2017) - [j65]Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt:
Planning in hybrid relational MDPs. Mach. Learn. 106(12): 1905-1932 (2017) - [j64]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: an algebraic Prolog for machine learning. Mach. Learn. 106(12): 1933-1969 (2017) - [j63]Thanh Le Van, Siegfried Nijssen, Matthijs van Leeuwen, Luc De Raedt:
Semiring Rank Matrix Factorization. IEEE Trans. Knowl. Data Eng. 29(8): 1737-1750 (2017) - [c172]Sergey Paramonov, Samuel Kolb, Tias Guns, Luc De Raedt:
TaCLe: Learning Constraints in Tabular Data. CIKM 2017: 2511-2514 - [c171]Behrouz Babaki, Tias Guns, Luc De Raedt:
Stochastic Constraint Programming with And-Or Branch-and-Bound. IJCAI 2017: 539-545 - [c170]Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc De Raedt:
Solving Probability Problems in Natural Language. IJCAI 2017: 3981-3987 - [c169]Laura Antanas, Anton Dries, Plinio Moreno, Luc De Raedt:
Relational Affordance Learning for Task-Dependent Robot Grasping. ILP 2017: 1-15 - [c168]Gust Verbruggen, Luc De Raedt:
Towards Automated Relational Data Wrangling. AutoML@PKDD/ECML 2017: 12-20 - [r8]Luc De Raedt:
Inductive Logic Programming. Encyclopedia of Machine Learning and Data Mining 2017: 648-656 - [r7]Luc De Raedt:
Logic of Generality. Encyclopedia of Machine Learning and Data Mining 2017: 772-780 - [r6]Luc De Raedt:
Multi-relational Data Mining. Encyclopedia of Machine Learning and Data Mining 2017: 892-893 - [r5]Luc De Raedt, Kristian Kersting:
Statistical Relational Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1177-1187 - [i27]José Oramas M., Luc De Raedt, Tinne Tuytelaars:
Context-based Object Viewpoint Estimation: A 2D Relational Approach. CoRR abs/1704.06610 (2017) - [i26]Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt:
Sketched Answer Set Programming. CoRR abs/1705.07429 (2017) - 2016
- [b2]Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole:
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2016 - [j62]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Exploiting local and repeated structure in Dynamic Bayesian Networks. Artif. Intell. 232: 43-53 (2016) - [j61]Thanh Le Van, Matthijs van Leeuwen, Ana Carolina Fierro, Dries De Maeyer, Jimmy Van den Eynden, Lieven P. C. Verbeke, Luc De Raedt, Kathleen Marchal, Siegfried Nijssen:
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinform. 32(17): 445-454 (2016) - [j60]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
TP-Compilation for inference in probabilistic logic programs. Int. J. Approx. Reason. 78: 15-32 (2016) - [j59]Luc De Raedt, Yves Deville, Marc Bui, Truong Thi Dieu Linh:
Introduction to Special issue on "The Sixth International Symposium on Information and Communication Technology -SoICT 2015. Informatica (Slovenia) 40(2) (2016) - [j58]Davide Nitti, Tinne De Laet, Luc De Raedt:
Probabilistic logic programming for hybrid relational domains. Mach. Learn. 103(3): 407-449 (2016) - [c167]Jonas Vlasselaer, Angelika Kimmig, Anton Dries, Wannes Meert, Luc De Raedt:
Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. AAAI Workshop: Beyond NP 2016 - [c166]Davide Nitti, Irma Ravkic, Jesse Davis, Luc De Raedt:
Learning the Structure of Dynamic Hybrid Relational Models. ECAI 2016: 1283-1290 - [c165]Vincent Vercruyssen, Luc De Raedt, Jesse Davis:
Qualitative Spatial Reasoning for Soccer Pass Prediction. MLSA@PKDD/ECML 2016 - [p15]Luc De Raedt, Anton Dries, Tias Guns, Christian Bessiere:
Learning Constraint Satisfaction Problems: An ILP Perspective. Data Mining and Constraint Programming 2016: 96-112 - [p14]Anton Dries, Tias Guns, Siegfried Nijssen, Behrouz Babaki, Thanh Le Van, Benjamin Négrevergne, Sergey Paramonov, Luc De Raedt:
Modeling in MiningZinc. Data Mining and Constraint Programming 2016: 257-281 - [p13]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. Data Mining and Constraint Programming 2016: 303-309 - [e13]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. Lecture Notes in Computer Science 10101, Springer 2016, ISBN 978-3-319-50136-9 [contents] - [i25]Vaishak Belle, Luc De Raedt:
Semiring Programming: A Framework for Search, Inference and Learning. CoRR abs/1609.06954 (2016) - [i24]Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt:
Flexible constrained sampling with guarantees for pattern mining. CoRR abs/1610.09263 (2016) - 2015
- [j57]Luc De Raedt, Angelika Kimmig:
Probabilistic (logic) programming concepts. Mach. Learn. 100(1): 5-47 (2015) - [j56]Dries De Maeyer, Bram Weytjens, Joris Renkens, Luc De Raedt, Kathleen Marchal:
PheNetic: network-based interpretation of molecular profiling data. Nucleic Acids Res. 43(Webserver-Issue): W244-W250 (2015) - [j55]James Cussens, Luc De Raedt, Angelika Kimmig, Taisuke Sato:
Introduction to the special issue on probability, logic and learning. Theory Pract. Log. Program. 15(2): 145-146 (2015) - [j54]Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Sht. Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt:
Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory Pract. Log. Program. 15(3): 358-401 (2015) - [c164]Luc De Raedt:
Languages for Learning and Mining. AAAI 2015: 4107-4111 - [c163]Artur S. d'Avila Garcez, Tarek R. Besold, Luc De Raedt, Peter Földiák, Pascal Hitzler, Thomas Icard, Kai-Uwe Kühnberger, Luís C. Lamb, Risto Miikkulainen, Daniel L. Silver:
Neural-Symbolic Learning and Reasoning: Contributions and Challenges. AAAI Spring Symposia 2015 - [c162]Behrouz Babaki, Tias Guns, Siegfried Nijssen, Luc De Raedt:
Constraint-Based Querying for Bayesian Network Exploration. IDA 2015: 13-24 - [c161]Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke:
Inducing Probabilistic Relational Rules from Probabilistic Examples. IJCAI 2015: 1835-1843 - [c160]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. IJCAI 2015: 1852-1858 - [c159]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
Graph Invariant Kernels. IJCAI 2015: 3756-3762 - [c158]Paolo Frasconi, Fabrizio Costa, Luc De Raedt, Kurt De Grave:
kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract). IJCAI 2015: 4183-4187 - [c157]Laura Antanas, Plinio Moreno, Luc De Raedt:
Relational Kernel-Based Grasping with Numerical Features. ILP 2015: 1-14 - [c156]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: An Algebraic Prolog for Kernel Programming. ILP 2015: 152-165 - [c155]Sergey Paramonov, Matthijs van Leeuwen, Marc Denecker, Luc De Raedt:
An Exercise in Declarative Modeling for Relational Query Mining. ILP 2015: 166-182 - [c154]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Rank Matrix Factorisation. PAKDD (1) 2015: 734-746 - [c153]Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, Luc De Raedt:
ProbLog2: Probabilistic Logic Programming. ECML/PKDD (3) 2015: 312-315 - [c152]