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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
- 2024
[j97]Giuseppe Marra
, Sebastijan Dumancic, Robin Manhaeve, Luc De Raedt:
From statistical relational to neurosymbolic artificial intelligence: A survey. Artif. Intell. 328: 104062 (2024)- [j96]Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig:
Declarative probabilistic logic programming in discrete-continuous domains. Artif. Intell. 337: 104227 (2024) - [j95]Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt:
Semirings for probabilistic and neuro-symbolic logic programming. Int. J. Approx. Reason. 171: 109130 (2024) - [j94]Victor Verreet, Luc De Raedt, Jessa Bekker:
Modeling PU learning using probabilistic logic programming. Mach. Learn. 113(3): 1351-1372 (2024) - [c221]Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt:
SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge. AAAI 2024: 20123-20133 - [c220]Gabriele Venturato, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt:
Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation. AAAI 2024: 20567-20576 - [c219]Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt:
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments. LREC/COLING 2024: 3297-3313 - [c218]Sieben Bocklandt, Vincent Derkinderen, Angelika Kimmig, Luc De Raedt:
Approximate Compression of CNF Concepts. DS (2) 2024: 149-164 - [c217]Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone:
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic. ECAI 2024: 1349-1356 - [c216]Jaron Maene, Vincent Derkinderen, Luc De Raedt:
On the Hardness of Probabilistic Neurosymbolic Learning. ICML 2024 - [c215]Adem Kikaj, Giuseppe Marra, Luc De Raedt:
Subgraph Mining for Graph Neural Networks. IDA (1) 2024: 141-152 - [c214]Pedro Zuidberg Dos Martires, Vincent Derkinderen, Luc De Raedt, Marcus Krantz:
Automated Reasoning in Systems Biology: A Necessity for Precision Medicine. KR 2024 - [c213]Ying Jiao, Luc De Raedt, Giuseppe Marra:
Valid Text-to-SQL Generation with Unification-Based DeepStochLog. NeSy (1) 2024: 312-330 - [i68]Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt:
Semirings for Probabilistic and Neuro-Symbolic Logic Programming. CoRR abs/2402.13782 (2024) - [i67]Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt:
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments. CoRR abs/2403.03203 (2024) - [i66]Jaron Maene, Vincent Derkinderen, Luc De Raedt:
On the Hardness of Probabilistic Neurosymbolic Learning. CoRR abs/2406.04472 (2024) - [i65]Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires, Luc De Raedt:
Can Large Language Models Reason? A Characterization via 3-SAT. CoRR abs/2408.07215 (2024) - [i64]Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone:
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic. CoRR abs/2408.08133 (2024) - [i63]Pedro Zuidberg Dos Martires, Vincent Derkinderen, Luc De Raedt, Marcus Krantz:
Automated Reasoning in Systems Biology: a Necessity for Precision Medicine. CoRR abs/2410.13487 (2024) - [i62]Nikolaos Manginas, George Paliouras, Luc De Raedt:
NeSyA: Neurosymbolic Automata. CoRR abs/2412.07331 (2024) - [i61]Lennert De Smet, Gabriele Venturato, Luc De Raedt, Giuseppe Marra:
Relational Neurosymbolic Markov Models. CoRR abs/2412.13023 (2024) - 2023
- [j93]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from contextual examples for combinatorial optimisation. Artif. Intell. 314: 103794 (2023) - [j92]Pietro Totis, Jesse Davis, Luc De Raedt, Angelika Kimmig:
Lifted Reasoning for Combinatorial Counting. J. Artif. Intell. Res. 76: 1-58 (2023) - [j91]Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis:
A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer. J. Artif. Intell. Res. 77: 517-562 (2023) - [j90]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. J. Artif. Intell. Res. 77: 683-735 (2023) - [j89]Thomas Eiter, Michael J. Maher, Enrico Pontelli, Luc De Raedt, Miroslaw Truszczynski:
The Collection of Papers Celebrating the 20th Anniversary of TPLP, Part II. Theory Pract. Log. Program. 23(1): 1 (2023) - [j88]Pietro Totis, Luc De Raedt, Angelika Kimmig:
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation. Theory Pract. Log. Program. 23(6): 1198-1247 (2023) - [c212]Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt:
Safe Reinforcement Learning via Probabilistic Logic Shields. IJCAI 2023: 5739-5749 - [c211]Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt:
Safe Reinforcement Learning via Probabilistic Logic Shields. NeSy 2023: 428-429 - [c210]Jaron Maene, Luc De Raedt:
Soft-Unification in Deep Probabilistic Logic. NeurIPS 2023 - [c209]Rishi Hazra, Luc De Raedt:
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. ECML/PKDD (4) 2023: 213-229 - [c208]Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt:
Neural probabilistic logic programming in discrete-continuous domains. UAI 2023: 529-538 - [i60]Pedro Zuidberg Dos Martires, Luc De Raedt, Angelika Kimmig:
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains. CoRR abs/2302.10674 (2023) - [i59]Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt:
Safe Reinforcement Learning via Probabilistic Logic Shields. CoRR abs/2303.03226 (2023) - [i58]Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt:
Neural Probabilistic Logic Programming in Discrete-Continuous Domains. CoRR abs/2303.04660 (2023) - [i57]Pietro Totis, Angelika Kimmig, Luc De Raedt:
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation. CoRR abs/2304.00879 (2023) - [i56]Rishi Hazra, Luc De Raedt:
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. CoRR abs/2304.08349 (2023) - [i55]Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt:
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge. CoRR abs/2308.12682 (2023) - [i54]Luc De Raedt, Ute Schmid, Johannes Langer:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442). Dagstuhl Reports 13(10): 182-211 (2023) - 2022
- [j87]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) - [j86]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) - [j85]Wen-Chi Yang, Jean-François Raskin, Luc De Raedt:
Lifted model checking for relational MDPs. Mach. Learn. 111(10): 3797-3838 (2022) - [j84]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. 22(1): 81-114 (2022) - [j83]Thomas Eiter, Michael J. Maher, Enrico Pontelli, Luc De Raedt, Miroslaw Truszczynski:
Introduction to the Collection of Papers Celebrating the 20th Anniversary of TPLP. Theory Pract. Log. Program. 22(6): 770-775 (2022) - [c207]Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt:
Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. AAAI 2022: 10060-10069 - [c206]Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt:
DeepStochLog: Neural Stochastic Logic Programming. AAAI 2022: 10090-10100 - [c205]Wen-Chi Yang, Arcchit Jain, Luc De Raedt, Wannes Meert:
Parameter Learning in ProbLog with Annotated Disjunctions. IDA 2022: 378-391 - [p17]Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt:
Human-Machine Collaboration for Democratizing Data Science. Human-Like Machine Intelligence 2022: 379-402 - [e14]Luc De Raedt:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022. ijcai.org 2022, ISBN 978-1-956792-00-3 [contents] - [i53]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. CoRR abs/2201.11165 (2022) - [i52]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. CoRR abs/2202.03888 (2022) - [i51]Gavin Rens, Wen-Chi Yang, Jean-François Raskin, Luc De Raedt:
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains. CoRR abs/2211.03461 (2022) - 2021
- [j82]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
Neural probabilistic logic programming in DeepProbLog. Artif. Intell. 298: 103504 (2021) - [c204]Mohit Kumar, Samuel Kolb, Clément Gautrais, Luc De Raedt:
Democratizing Constraint Satisfaction Problems through Machine Learning. AAAI 2021: 16057-16059 - [c203]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 - [c202]Gillis Hermans, Thomas Winters, Luc De Raedt:
Shape Inference and Grammar Induction for Example-Based Procedural Generation. ICCC 2021: 342-349 - [c201]Gust Verbruggen, Elia Van Wolputte, Sebastijan Dumancic, Luc De Raedt:
avatar - Automated Feature Wrangling for Machine Learning. IDA 2021: 235-247 - [c200]Gust Verbruggen, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Luc De Raedt:
Muppets: Multipurpose Table Segmentation. IDA 2021: 389-401 - [c199]Dirko Coetsee, Steve Kroon, McElory Hoffmann, Luc De Raedt:
SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints. IDA 2021: 402-413 - [c198]Arcchit Jain, Clément Gautrais, Angelika Kimmig, Luc De Raedt:
Learning CNF Theories Using MDL and Predicate Invention. IJCAI 2021: 2599-2605 - [c197]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
- [j81]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) - [j80]Vaishak Belle, Luc De Raedt:
Semiring programming: A semantic framework for generalized sum product problems. Int. J. Approx. Reason. 126: 181-201 (2020) - [j79]Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt:
Predictive spreadsheet autocompletion with constraints. Mach. Learn. 109(2): 307-325 (2020) - [j78]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) - [c196]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. AAAI 2020: 4493-4500 - [c195]Vincent Derkinderen, Luc De Raedt:
Algebraic Circuits for Decision Theoretic Inference and Learning. ECAI 2020: 2569-2576 - [c194]Thomas Winters, Luc De Raedt:
Discovering Textual Structures: Generative Grammar Induction using Template Trees. ICCC 2020: 177-180 - [c193]Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, Giuseppe Marra:
From Statistical Relational to Neuro-Symbolic Artificial Intelligence. IJCAI 2020: 4943-4950 - [c192]Andreas Persson, Pedro Zuidberg Dos Martires, Luc De Raedt, Amy Loutfi:
ProbAnch: a Modular Probabilistic Anchoring Framework. IJCAI 2020: 5285-5287 - [c191]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 - [c190]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
- [j77]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) - [c189]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 - [c188]Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt:
Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC 2019 - [c187]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. BNAIC/BENELEARN 2019 - [c186]Mohit Kumar, Stefano Teso, Patrick De Causmaecker, Luc De Raedt:
Automating Personnel Rostering by Learning Constraints Using Tensors. ICTAI 2019: 697-704 - [c185]Mohit Kumar, Stefano Teso, Luc De Raedt:
Acquiring Integer Programs from Data. IJCAI 2019: 1130-1136 - [c184]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 - [c183]Luc De Raedt, Robin Manhaeve, Sebastijan Dumancic, Thomas Demeester, Angelika Kimmig:
Neuro-Symbolic = Neural + Logical + Probabilistic. NeSy@IJCAI 2019 - [c182]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 - [c181]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
- [j76]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) - [c180]Luc De Raedt, Andrea Passerini, Stefano Teso:
Learning Constraints From Examples. AAAI 2018: 7965-7970 - [c179]Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt:
Sketched Answer Set Programming. ICTAI 2018: 694-701 - [c178]Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen:
Elements of an Automatic Data Scientist. IDA 2018: 3-14 - [c177]Gust Verbruggen, Luc De Raedt:
Automatically Wrangling Spreadsheets into Machine Learning Data Formats. IDA 2018: 367-379 - [c176]Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt:
Learning SMT(LRA) Constraints using SMT Solvers. IJCAI 2018: 2333-2340 - [c175]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
- [j75]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) - [j74]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) - [j73]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) - [j72]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) - [j71]Luc De Raedt, Marc Bui, Yves Deville, T. Dieu Linh Truong:
Editors' Introduction to the Special Issue on "Information and Communication Technology". Informatica (Slovenia) 41(2) (2017) - [j70]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic model counting. J. Appl. Log. 22: 46-62 (2017) - [j69]Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt:
Learning constraints in spreadsheets and tabular data. Mach. Learn. 106(9-10): 1441-1468 (2017) - [j68]Sergey Paramonov, Matthijs van Leeuwen, Luc De Raedt:
Relational data factorization. Mach. Learn. 106(12): 1867-1904 (2017) - [j67]Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt:
Planning in hybrid relational MDPs. Mach. Learn. 106(12): 1905-1932 (2017) - [j66]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: an algebraic Prolog for machine learning. Mach. Learn. 106(12): 1933-1969 (2017) - [j65]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) - [c174]Sergey Paramonov, Samuel Kolb, Tias Guns, Luc De Raedt:
TaCLe: Learning Constraints in Tabular Data. CIKM 2017: 2511-2514 - [c173]Behrouz Babaki, Tias Guns, Luc De Raedt:
Stochastic Constraint Programming with And-Or Branch-and-Bound. IJCAI 2017: 539-545 - [c172]Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc De Raedt:
Solving Probability Problems in Natural Language. IJCAI 2017: 3981-3987 - [c171]Laura Antanas, Anton Dries, Plinio Moreno, Luc De Raedt:
Relational Affordance Learning for Task-Dependent Robot Grasping. ILP 2017: 1-15 - [c170]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, ISBN 978-3-031-00022-5 - [j64]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) - [j63]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) - [j62]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) - [j61]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) - [j60]Davide Nitti, Tinne De Laet, Luc De Raedt:
Probabilistic logic programming for hybrid relational domains. Mach. Learn. 103(3): 407-449 (2016) - [c169]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 - [c168]Davide Nitti, Irma Ravkic, Jesse Davis, Luc De Raedt:
Learning the Structure of Dynamic Hybrid Relational Models. ECAI 2016: 1283-1290 - [c167]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
- [j59]Luc De Raedt, Angelika Kimmig:
Probabilistic (logic) programming concepts. Mach. Learn. 100(1): 5-47 (2015) - [j58]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) - [j57]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) - [j56]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) - [c166]Luc De Raedt:
Languages for Learning and Mining. AAAI 2015: 4107-4111 - [c165]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 - [c164]Behrouz Babaki, Tias Guns, Siegfried Nijssen, Luc De Raedt:
Constraint-Based Querying for Bayesian Network Exploration. IDA 2015: 13-24 - [c163]Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke:
Inducing Probabilistic Relational Rules from Probabilistic Examples. IJCAI 2015: 1835-1843 - [c162]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 - [c161]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
Graph Invariant Kernels. IJCAI 2015: 3756-3762 - [c160]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 - [c159]Laura Antanas, Plinio Moreno, Luc De Raedt:
Relational Kernel-Based Grasping with Numerical Features. ILP 2015: 1-14 - [c158]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: An Algebraic Prolog for Kernel Programming. ILP 2015: 152-165 - [c157]Sergey Paramonov, Matthijs van Leeuwen, Marc Denecker, Luc De Raedt:
An Exercise in Declarative Modeling for Relational Query Mining. ILP 2015: 166-182 - [c156]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Rank Matrix Factorisation. PAKDD (1) 2015: 734-746 - [c155]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 - [c154]Davide Nitti, Vaishak Belle, Luc De Raedt:
Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. ECML/PKDD (2) 2015: 327-342 - [e12]Huynh Quyet Thang, Le Anh Phuong, Luc De Raedt, Yves Deville, Marc Bui, Truong Thi Dieu Linh, Thi-Oanh Nguyen, Dinh Viet Sang, Nguyen Ba Ngoc:
Proceedings of the Sixth International Symposium on Information and Communication Technology, Hue City, Vietnam, December 3-4, 2015. ACM 2015, ISBN 978-1-4503-3843-1 [contents] - [i23]Albrecht Zimmermann, Björn Bringmann, Luc De Raedt:
Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling. CoRR abs/1501.03015 (2015) - [i22]Joris Renkens, Angelika Kimmig, Luc De Raedt:
Lazy Explanation-Based Approximation for Probabilistic Logic Programming. CoRR abs/1507.02873 (2015) - [i21]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. CoRR abs/1510.03317 (2015) - 2014
- [j55]Paolo Frasconi, Fabrizio Costa, Luc De Raedt, Kurt De Grave:
kLog: A language for logical and relational learning with kernels. Artif. Intell. 217: 117-143 (2014) - [j54]Maria Fox, Luc De Raedt:
Introduction to the Special Issue on the ECAI 2012 Turing and Anniversary Track. AI Commun. 27(1): 1 (2014) - [j53]Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Interactive Learning of Pattern Rankings. Int. J. Artif. Intell. Tools 23(6) (2014) - [j52]Laura Antanas, Martijn van Otterlo, José Oramas Mogrovejo, Tinne Tuytelaars, Luc De Raedt:
There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding. Neurocomputing 123: 75-85 (2014) - [c153]Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. AAAI 2014: 2490-2496 - [c152]Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. StarAI@AAAI 2014 - [c151]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Efficient Probabilistic Inference for Dynamic Relational Models. StarAI@AAAI 2014 - [c150]Mathias Verbeke, Paolo Frasconi, Kurt De Grave, Fabrizio Costa, Luc De Raedt:
kLogNLP: Graph Kernel-based Relational Learning of Natural Language. ACL (System Demonstrations) 2014: 85-90 - [c149]Jonas Vlasselaer, Wannes Meert, Rocco Langone, Luc De Raedt:
Condition Monitoring with Incomplete Observations. ECAI 2014: 1215-1216 - [c148]Bogdan Moldovan, Luc De Raedt:
Occluded object search by relational affordances. ICRA 2014: 169-174 - [c147]Davide Nitti, Tinne De Laet, Luc De Raedt:
Relational object tracking and learning. ICRA 2014: 935-942 - [c146]Dries Van Daele, Angelika Kimmig, Luc De Raedt:
PageRank, ProPPR, and Stochastic Logic Programs. ILP 2014: 168-180 - [c145]Bogdan Moldovan, Luc De Raedt:
Learning relational affordance models for two-arm robots. IROS 2014: 2916-2922 - [c144]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Ana Carolina Fierro, Kathleen Marchal, Luc De Raedt:
Ranked Tiling. ECML/PKDD (2) 2014: 98-113 - [c143]Davide Nitti, Tinne De Laet, Luc De Raedt:
Distributional Clauses Particle Filter. ECML/PKDD (3) 2014: 504-507 - [c142]Fabrizio Costa, Mathias Verbeke, Luc De Raedt:
Relational Regularization and Feature Ranking. SDM 2014: 650-658 - [c141]Mathias Verbeke, Vincent Van Asch, Walter Daelemans, Luc De Raedt:
Lazy and Eager Relational Learning Using Graph-Kernels. SLSP 2014: 171-184 - [c140]Davide Nitti, Georgios Chliveros, Maria Pateraki, Luc De Raedt, Emmanouil Hourdakis, Panos E. Trahanias:
Application of Dynamic Distributional Clauses for Multi-hypothesis Initialization in Model-based Object Tracking. VISAPP (2) 2014: 256-261 - [c139]José Oramas M., Luc De Raedt, Tinne Tuytelaars:
Towards cautious collective inference for object verification. WACV 2014: 269-276 - [i20]Laura Antanas, Plinio Moreno, Marion Neumann, Rui Pimentel de Figueiredo, Kristian Kersting, José Santos-Victor, Luc De Raedt:
High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline. CoRR abs/1411.1108 (2014) - [i19]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Michèle Sebag:
Constraints, Optimization and Data (Dagstuhl Seminar 14411). Dagstuhl Reports 4(10): 1-31 (2014) - 2013
- [j51]Gemma C. Garriga, Roni Khardon, Luc De Raedt:
Mining closed patterns in relational, graph and network data. Ann. Math. Artif. Intell. 69(4): 315-342 (2013) - [j50]Tias Guns, Siegfried Nijssen, Luc De Raedt:
k-Pattern Set Mining under Constraints. IEEE Trans. Knowl. Data Eng. 25(2): 402-418 (2013) - [c138]Martin Theobald, Luc De Raedt, Maximilian Dylla, Angelika Kimmig, Iris Miliaraki:
10 Years of Probabilistic Querying - What Next? ADBIS 2013: 1-13 - [c137]Bogdan Moldovan, Ingo Thon, Jesse Davis, Luc De Raedt:
MCMC Estimation of Conditional Probabilities in Probabilistic Programming Languages. ECSQARU 2013: 436-448 - [c136]Luc De Raedt:
Statistical Relational Learning using ProbLog. KNOW@LOD 2013: 1 - [c135]José Oramas M., Luc De Raedt, Tinne Tuytelaars:
Allocentric Pose Estimation. ICCV 2013: 289-296 - [c134]Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt:
The MiningZinc Framework for Constraint-Based Itemset Mining. ICDM Workshops 2013: 1081-1084 - [c133]Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Active Preference Learning for Ranking Patterns. ICTAI 2013: 532-539 - [c132]Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt:
MiningZinc: A Modeling Language for Constraint-Based Mining. IJCAI 2013: 1365-1372 - [c131]Davide Nitti, Tinne De Laet, Luc De Raedt:
A particle filter for hybrid relational domains. IROS 2013: 2764-2771 - [c130]Laura Antanas, McElory Hoffmann, Paolo Frasconi, Tinne Tuytelaars, Luc De Raedt:
A relational kernel-based approach to scene classification. WACV 2013: 133-139 - [i18]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. CoRR abs/1304.6810 (2013) - [i17]Luc De Raedt, Angelika Kimmig:
Probabilistic Programming Concepts. CoRR abs/1312.4328 (2013) - 2012
- [j49]Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan:
ILP turns 20 - Biography and future challenges. Mach. Learn. 86(1): 3-23 (2012) - [c129]Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining. ALT 2012: 12 - [c128]Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining. Discovery Science 2012: 1 - [c127]Mathias Verbeke, Vincent Van Asch, Roser Morante, Paolo Frasconi, Walter Daelemans, Luc De Raedt:
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories. EMNLP-CoNLL 2012: 579-589 - [c126]Thanh Le Van, Ana Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt, Kathleen Marchal:
Mining Local Staircase Patterns in Noisy Data. ICDM Workshops 2012: 139-146 - [c125]Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining. ICFCA 2012: 2 - [c124]Laura Antanas, Martijn van Otterlo, José Oramas M., Tinne Tuytelaars, Luc De Raedt:
A Relational Distance-based Framework for Hierarchical Image Understanding. ICPRAM (2) 2012: 206-218 - [c123]Bogdan Moldovan, Plinio Moreno, Martijn van Otterlo, José Santos-Victor, Luc De Raedt:
Learning relational affordance models for robots in multi-object manipulation tasks. ICRA 2012: 4373-4378 - [c122]Luc De Raedt:
Declarative Modeling for Machine Learning and Data Mining. ECML/PKDD (1) 2012: 2-3 - [c121]Laura Antanas, Paolo Frasconi, Fabrizio Costa, Tinne Tuytelaars, Luc De Raedt:
A Relational Kernel-Based Framework for Hierarchical Image Understanding. SSPR/SPR 2012: 171-180 - [c120]Davide Nitti, Tinne De Laet, McElory Hoffmann, Ingo Thon, Guy Van den Broeck, Luc De Raedt:
A Particle Filter for Probabilistic Dynamic Relational Domains. StarAI@UAI 2012 - [p12]Angelika Kimmig, Esther Galbrun, Hannu Toivonen, Luc De Raedt:
Patterns and Logic for Reasoning with Networks. Bisociative Knowledge Discovery 2012: 122-143 - [p11]Anton Dries, Siegfried Nijssen, Luc De Raedt:
BiQL: A Query Language for Analyzing Information Networks. Bisociative Knowledge Discovery 2012: 147-165 - [e11]Luc De Raedt, Christian Bessiere, Didier Dubois, Patrick Doherty, Paolo Frasconi, Fredrik Heintz, Peter J. F. Lucas:
ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, Montpellier, France, August 27-31 , 2012. Frontiers in Artificial Intelligence and Applications 242, IOS Press 2012, ISBN 978-1-61499-097-0 [contents] - [i16]Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt:
Inference in Probabilistic Logic Programs using Weighted CNF's. CoRR abs/1202.3719 (2012) - [i15]Paolo Frasconi, Fabrizio Costa, Luc De Raedt, Kurt De Grave:
kLog: A Language for Logical and Relational Learning with Kernels. CoRR abs/1205.3981 (2012) - [i14]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic Model Counting. CoRR abs/1211.4475 (2012) - 2011
- [j48]Tias Guns, Siegfried Nijssen, Luc De Raedt:
Itemset mining: A constraint programming perspective. Artif. Intell. 175(12-13): 1951-1983 (2011) - [j47]Ingo Thon, Niels Landwehr, Luc De Raedt:
Stochastic relational processes: Efficient inference and applications. Mach. Learn. 82(2): 239-272 (2011) - [j46]Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan:
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Mach. Learn. 83(2): 133-135 (2011) - [j45]Leander Schietgat, Fabrizio Costa, Jan Ramon, Luc De Raedt:
Effective feature construction by maximum common subgraph sampling. Mach. Learn. 83(2): 137-161 (2011) - [j44]Angelika Kimmig, Bart Demoen, Luc De Raedt, Vítor Santos Costa, Ricardo Rocha:
On the implementation of the probabilistic logic programming language ProbLog. Theory Pract. Log. Program. 11(2-3): 235-262 (2011) - [j43]Bernd Gutmann, Ingo Thon, Angelika Kimmig, Maurice Bruynooghe, Luc De Raedt:
The magic of logical inference in probabilistic programming. Theory Pract. Log. Program. 11(4-5): 663-680 (2011) - [c119]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
An Algebraic Prolog for Reasoning about Possible Worlds. AAAI 2011: 209-214 - [c118]Tias Guns, Siegfried Nijssen, Albrecht Zimmermann, Luc De Raedt:
Declarative Heuristic Search for Pattern Set Mining. ICDM Workshops 2011: 1104-1111 - [c117]Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt:
Lifted Probabilistic Inference by First-Order Knowledge Compilation. IJCAI 2011: 2178-2185 - [c116]Bogdan Moldovan, Martijn van Otterlo, Luc De Raedt, Plinio Moreno, José Santos-Victor:
Statistical Relational Learning of Object Affordances for Robotic Manipulation. ILP (Late Breaking Papers) 2011: 95-103 - [c115]Parisa Kordjamshidi, Paolo Frasconi, Martijn van Otterlo, Marie-Francine Moens, Luc De Raedt:
Relational Learning for Spatial Relation Extraction from Natural Language. ILP 2011: 204-220 - [c114]Mathias Verbeke, Paolo Frasconi, Vincent Van Asch, Roser Morante, Walter Daelemans, Luc De Raedt:
Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection. ILP 2011: 347-357 - [c113]Luc De Raedt, Siegfried Nijssen:
Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract). ISMIS 2011: 25-32 - [c112]Tias Guns, Siegfried Nijssen, Luc De Raedt:
Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. PAKDD (2) 2011: 382-394 - [c111]Bernd Gutmann, Ingo Thon, Luc De Raedt:
Learning the Parameters of Probabilistic Logic Programs from Interpretations. ECML/PKDD (1) 2011: 581-596 - [c110]Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt:
Inference in Probabilistic Logic Programs using Weighted CNF's. UAI 2011: 211-220 - [i13]Bernd Gutmann, Ingo Thon, Angelika Kimmig, Maurice Bruynooghe, Luc De Raedt:
The Magic of Logical Inference in Probabilistic Programming. CoRR abs/1107.5152 (2011) - [i12]Luc De Raedt, Kristian Kersting, Tapani Raiko:
Logical Hidden Markov Models. CoRR abs/1109.2148 (2011) - [i11]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Pascal Van Hentenryck:
Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201). Dagstuhl Reports 1(5): 61-83 (2011) - 2010
- [j42]Niels Landwehr, Andrea Passerini, Luc De Raedt, Paolo Frasconi:
Fast learning of relational kernels. Mach. Learn. 78(3): 305-342 (2010) - [j41]Anton Dries, Luc De Raedt, Siegfried Nijssen:
Mining Predictive k-CNF Expressions. IEEE Trans. Knowl. Data Eng. 22(5): 743-748 (2010) - [c109]Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, Luc De Raedt:
DTProbLog: A Decision-Theoretic Probabilistic Prolog. AAAI 2010: 1217-1222 - [c108]Luc De Raedt, Tias Guns, Siegfried Nijssen:
Constraint Programming for Data Mining and Machine Learning. AAAI 2010: 1671-1675 - [c107]Maurice Bruynooghe, Theofrastos Mantadelis, Angelika Kimmig, Bernd Gutmann, Joost Vennekens, Gerda Janssens, Luc De Raedt:
ProbLog Technology for Inference in a Probabilistic First Order Logic. ECAI 2010: 719-724 - [c106]Laura Antanas, Martijn van Otterlo, José Oramas M., Tinne Tuytelaars, Luc De Raedt:
Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses. ILP 2010: 22-29 - [c105]Luc De Raedt, Ingo Thon:
Probabilistic Rule Learning. ILP 2010: 47-58 - [c104]Bernd Gutmann, Manfred Jaeger, Luc De Raedt:
Extending ProbLog with Continuous Distributions. ILP 2010: 76-91 - [p10]Luc De Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila:
A Theory of Inductive Query Answering. Inductive Databases and Constraint-Based Data Mining 2010: 79-103 - [p9]Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen:
Probabilistic Inductive Querying Using ProbLog. Inductive Databases and Constraint-Based Data Mining 2010: 229-262 - [p8]Luc De Raedt:
About Knowledge and Inference in Logical and Relational Learning. Advances in Machine Learning II 2010: 143-153 - [e10]Luc De Raedt:
Inductive Logic Programming, 19th International Conference, ILP 2009, Leuven, Belgium, July 02-04, 2009. Revised Papers. Lecture Notes in Computer Science 5989, Springer 2010, ISBN 978-3-642-13839-3 [contents] - [r4]Luc De Raedt:
Inductive Logic Programming. Encyclopedia of Machine Learning 2010: 529-537 - [r3]Luc De Raedt:
Logic of Generality. Encyclopedia of Machine Learning 2010: 624-631 - [r2]Luc De Raedt:
Multi-Relational Data Mining. Encyclopedia of Machine Learning 2010: 711 - [r1]Luc De Raedt, Kristian Kersting:
Statistical Relational Learning. Encyclopedia of Machine Learning 2010: 916-924 - [i10]Angelika Kimmig, Bart Demoen, Luc De Raedt, Vítor Santos Costa, Ricardo Rocha:
On the Implementation of the Probabilistic Logic Programming Language ProbLog. CoRR abs/1006.4442 (2010)
2000 – 2009
- 2009
- [j40]Albrecht Zimmermann, Luc De Raedt:
Cluster-grouping: from subgroup discovery to clustering. Mach. Learn. 77(1): 125-159 (2009) - [j39]Luc De Raedt, Jan Ramon:
Deriving distance metrics from generality relations. Pattern Recognit. Lett. 30(3): 187-191 (2009) - [c103]Anton Dries, Siegfried Nijssen, Luc De Raedt:
A query language for analyzing networks. CIKM 2009: 485-494 - [c102]Luc De Raedt:
Constraint Programming for Data Mining. EGC 2009: 3 - [c101]Luc De Raedt:
The Logic of Learning. ICFCA 2009: 57 - [c100]Luc De Raedt:
Probabilistic Logic Learning - A Tutorial Abstract. ICLP 2009: 39 - [c99]Angelika Kimmig, Luc De Raedt:
Local Query Mining in a Probabilistic Prolog. IJCAI 2009: 1095-1100 - [c98]Anton Dries, Luc De Raedt:
Towards Clausal Discovery for Stream Mining. ILP 2009: 9-16 - [c97]Siegfried Nijssen, Tias Guns, Luc De Raedt:
Correlated itemset mining in ROC space: a constraint programming approach. KDD 2009: 647-656 - [c96]Siegfried Nijssen, Luc De Raedt:
Grammar Mining. SDM 2009: 1026-1037 - 2008
- [b1]Luc De Raedt:
Logical and relational learning. Cognitive Technologies, Springer 2008, ISBN 978-3-540-20040-6, pp. I-XV, 1-387 [contents] - [j38]Ulrich Rückert, Luc De Raedt:
An experimental evaluation of simplicity in rule learning. Artif. Intell. 172(1): 19-28 (2008) - [j37]Niels Landwehr, Bernd Gutmann, Ingo Thon, Luc De Raedt, Matthai Philipose:
Relational Transformation-based Tagging for Activity Recognition. Fundam. Informaticae 89(1): 111-129 (2008) - [j36]Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo, Hannu Toivonen:
Compressing probabilistic Prolog programs. Mach. Learn. 70(2-3): 151-168 (2008) - [c95]Kurt De Grave, Jan Ramon, Luc De Raedt:
Active Learning for High Throughput Screening. Discovery Science 2008: 185-196 - [c94]Angelika Kimmig, Vítor Santos Costa, Ricardo Rocha, Bart Demoen, Luc De Raedt:
On the Efficient Execution of ProbLog Programs. ICLP 2008: 175-189 - [c93]Luc De Raedt, Tias Guns, Siegfried Nijssen:
Constraint programming for itemset mining. KDD 2008: 204-212 - [c92]Bernd Gutmann, Angelika Kimmig, Kristian Kersting, Luc De Raedt:
Parameter Learning in Probabilistic Databases: A Least Squares Approach. ECML/PKDD (1) 2008: 473-488 - [c91]Ingo Thon, Niels Landwehr, Luc De Raedt:
A Simple Model for Sequences of Relational State Descriptions. ECML/PKDD (2) 2008: 506-521 - [c90]Luc De Raedt:
Logical and Relational Learning. SBIA 2008: 1 - [c89]Luc De Raedt:
Logic, Probability and Learning, or an Introduction to Statistical Relational Learning. SBIA 2008: 5 - [p7]Luc De Raedt, Kristian Kersting:
Probabilistic Inductive Logic Programming. Probabilistic Inductive Logic Programming 2008: 1-27 - [p6]Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr:
Relational Sequence Learning. Probabilistic Inductive Logic Programming 2008: 28-55 - [p5]Kristian Kersting, Luc De Raedt:
Basic Principles of Learning Bayesian Logic Programs. Probabilistic Inductive Logic Programming 2008: 189-221 - [e9]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007. Dagstuhl Seminar Proceedings 07161, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - [e8]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
Recurrent Neural Networks - Models, Capacities, and Applications, 20.01. - 25.01.2008. Dagstuhl Seminar Proceedings 08041, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - [e7]Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton:
Probabilistic Inductive Logic Programming - Theory and Applications. Lecture Notes in Computer Science 4911, Springer 2008, ISBN 978-3-540-78651-1 [contents] - [i9]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
08041 Summary -- Recurrent Neural Networks - Models, Capacities, and Applications. Recurrent Neural Networks 2008 - [i8]Luc De Raedt, Barbara Hammer, Pascal Hitzler, Wolfgang Maass:
08041 Abstracts Collection -- Recurrent Neural Networks - Models, Capacities, and Applications. Recurrent Neural Networks 2008 - 2007
- [j35]Kristian Kersting, Christian Plagemann, Alexandru Cocora, Wolfram Burgard, Luc De Raedt:
Learning to transfer optimal navigation policies. Adv. Robotics 21(13): 1565-1582 (2007) - [j34]Tayfun Gürel, Luc De Raedt, Stefan Rotter:
Ranking neurons for mining structure-activity relations in biological neural networks: NeuronRank. Neurocomputing 70(10-12): 1897-1901 (2007) - [j33]Niels Landwehr, Kristian Kersting, Luc De Raedt:
Integrating Naïve Bayes and FOIL. J. Mach. Learn. Res. 8: 481-507 (2007) - [c88]Angelika Kimmig, Luc De Raedt, Hannu Toivonen:
Probabilistic Explanation Based Learning. ECML 2007: 176-187 - [c87]Gemma C. Garriga, Roni Khardon, Luc De Raedt:
On Mining Closed Sets in Multi-Relational Data. IJCAI 2007: 804-809 - [c86]Niels Landwehr, Luc De Raedt:
r-grams: Relational Grams. IJCAI 2007: 907-912 - [c85]Luc De Raedt:
Statistical Relational Learning - A Logical Approach (Abstract of Invited Talk). NeSy 2007 - [c84]Luc De Raedt, Angelika Kimmig, Hannu Toivonen:
ProbLog: A Probabilistic Prolog and Its Application in Link Discovery. IJCAI 2007: 2462-2467 - [c83]Tayfun Gürel, Ulrich Egert, Steffen Kandler, Luc De Raedt, Stefan Rotter:
Predicting Spike Activity in Neuronal Cultures. IJCNN 2007: 2942-2947 - [c82]Luc De Raedt:
ProbLog and its Application to Link Mining in Biological Networks. MLG 2007 - [c81]Luc De Raedt, Albrecht Zimmermann:
Constraint-Based Pattern Set Mining. SDM 2007: 237-248 - [p4]Tayfun Gürel, Luc De Raedt, Stefan Rotter:
Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank. Perspectives of Neural-Symbolic Integration 2007: 49-65 - [i7]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [j32]Kristian Kersting, Luc De Raedt, Tapani Raiko:
Logical Hidden Markov Models. J. Artif. Intell. Res. 25: 425-456 (2006) - [j31]Andreas Karwath, Luc De Raedt:
SMIREP: Predicting Chemical Activity from SMILES. J. Chem. Inf. Model. 46(6): 2432-2444 (2006) - [j30]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. J. Mach. Learn. Res. 7: 307-342 (2006) - [j29]Alexandru Cocora, Kristian Kersting, Wolfram Burgard, Luc De Raedt, Christian Plagemann:
Learning Relational Navigation Policies. Künstliche Intell. 20(3): 12-18 (2006) - [j28]Rolf Backofen, Hans-Gunther Borrmann, Werner Deck, Andreas Dedner, Luc De Raedt, Klaus Desch, Markus Diesmann, Martin Geier, Andreas Greiner, Wolfgang R. Hess, Josef Honerkamp, Stefan Jankowski, Ingo Krossing, Andreas W. Liehr, Andreas Karwath, Robert Klöfkorn, Raphaël Pesché, Tobias C. Potjans, Michael C. Röttger, Lars Schmidt-Thieme, Gerhard Schneider, Björn Voß, Bernd Wiebelt, Peter Wienemann, Volker-Henning Winterer:
A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. Prax. Inf.verarb. Kommun. 29(2): 81-87 (2006) - [c80]Niels Landwehr, Andrea Passerini, Luc De Raedt, Paolo Frasconi:
kFOIL: Learning Simple Relational Kernels. AAAI 2006: 389-394 - [c79]Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo, Hannu Toivonen:
Revising Probabilistic Prolog Programs. ILP 2006: 30-33 - [c78]Tamás Horváth, Björn Bringmann, Luc De Raedt:
Frequent Hypergraph Mining. ILP 2006: 244-259 - [c77]Alexandru Cocora, Kristian Kersting, Christian Plagemann, Wolfram Burgard, Luc De Raedt:
Learning Relational Navigation Policies. IROS 2006: 2792-2797 - [c76]Jérémy Besson, Céline Robardet, Luc De Raedt, Jean-François Boulicaut:
Mining Bi-sets in Numerical Data. KDID 2006: 11-23 - [c75]Siegfried Nijssen, Luc De Raedt:
IQL: A Proposal for an Inductive Query Language. KDID 2006: 189-207 - [c74]Björn Bringmann, Albrecht Zimmermann, Luc De Raedt, Siegfried Nijssen:
Don't Be Afraid of Simpler Patterns. PKDD 2006: 55-66 - [e6]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005. Dagstuhl Seminar Proceedings 05051, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006 [contents] - 2005
- [j27]Takashi Washio, Luc De Raedt, Joost N. Kok:
Advances in Mining Graphs, Trees and Sequences. Fundam. Informaticae 66(1-2) (2005) - [c73]Luc De Raedt, Kristian Kersting, Sunna Torge:
Towards Learning Stochastic Logic Programs from Proof-Banks. AAAI 2005: 752-757 - [c72]Niels Landwehr, Kristian Kersting, Luc De Raedt:
nFOIL: Integrating Naïve Bayes and FOIL. AAAI 2005: 795-800 - [c71]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. AAIP 2005: 37-48 - [c70]Christian Stolle, Andreas Karwath, Luc De Raedt:
CLASSIC'CL: An Integrated ILP System. Discovery Science 2005: 354-362 - [c69]Luc De Raedt:
Statistical Relational Learning: An Inductive Logic Programming Perspective. PKDD 2005: 3-5 - [e5]Jean-François Boulicaut, Luc De Raedt, Heikki Mannila:
Constraint-Based Mining and Inductive Databases, European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers. Lecture Notes in Computer Science 3848, Springer 2005, ISBN 3-540-31331-1 [contents] - [e4]Luc De Raedt, Stefan Wrobel:
Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005. ACM International Conference Proceeding Series 119, ACM 2005, ISBN 1-59593-180-5 [contents] - [i6]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. Probabilistic, Logical and Relational Learning 2005 - [i5]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - [i4]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - 2004
- [j26]Christoph Helma, Tobias Cramer, Stefan Kramer, Luc De Raedt:
Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds. J. Chem. Inf. Model. 44(4): 1402-1411 (2004) - [c68]Luc De Raedt, Kristian Kersting:
Probabilistic Inductive Logic Programming. ALT 2004: 19-36 - [c67]Albrecht Zimmermann, Luc De Raedt:
Inductive Querying for Discovering Subgroups and Clusters. Constraint-Based Mining and Inductive Databases 2004: 380-399 - [c66]Andreas Karwath, Luc De Raedt:
Predictive Graph Mining. Discovery Science 2004: 1-15 - [c65]Albrecht Zimmermann, Luc De Raedt:
CorClass: Correlated Association Rule Mining for Classification. Discovery Science 2004: 60-72 - [c64]Albrecht Zimmermann, Luc De Raedt:
Cluster-Grouping: From Subgroup Discovery to Clustering. ECML 2004: 575-577 - [c63]Kristian Kersting, Martijn van Otterlo, Luc De Raedt:
Bellman goes relational. ICML 2004 - [c62]Kristian Kersting, Luc De Raedt:
Logical Markov Decision Programs and the Convergence of Logical TD(lambda). ILP 2004: 180-197 - [c61]Sau Dan Lee, Luc De Raedt:
An Efficient Algorithm for Mining String Databases Under Constraints. KDID 2004: 108-129 - [c60]Luc De Raedt, Jan Ramon:
Condensed Representations for Inductive Logic Programming. KR 2004: 438-446 - [c59]Johannes Fischer, Luc De Raedt:
Towards Optimizing Conjunctive Inductive Queries. PAKDD 2004: 625-637 - [p3]Luc De Raedt:
Towards Query Evaluation in Inductive Databases Using Version Spaces. Database Support for Data Mining Applications 2004: 117-134 - [p2]Sau Dan Lee, Luc De Raedt:
Constraint Based Mining of First Order Sequences in SeqLog. Database Support for Data Mining Applications 2004: 154-173 - 2003
- [j25]Luc De Raedt, Kristian Kersting:
Probabilistic logic learning. SIGKDD Explor. 5(1): 31-48 (2003) - [j24]Saso Dzeroski, Luc De Raedt:
Multi-relational data mining: the current frontiers. SIGKDD Explor. 5(1): 100-101 (2003) - [j23]Saso Dzeroski, Luc De Raedt, Stefan Wrobel:
Multirelational data mining 2003: workshop report. SIGKDD Explor. 5(2): 200-202 (2003) - [c58]Sau Dan Lee, Luc De Raedt:
An Algebra for Inductive Query Evaluation. ICDM 2003: 147-154 - [c57]Johannes Fischer, Luc De Raedt:
Towards Optimizing Conjunctive Inductive Queries. KDID 2003: 44-59 - [c56]Sau Dan Lee, Luc De Raedt:
An Algebra for Inductive Query Evaluation. KDID 2003: 80-96 - [c55]Kristian Kersting, Tapani Raiko, Stefan Kramer, Luc De Raedt:
Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models. Pacific Symposium on Biocomputing 2003: 192-203 - 2002
- [j22]Luc De Raedt:
A Perspective on Inductive Databases. SIGKDD Explor. 4(2): 69-77 (2002) - [j21]Saso Dzeroski, Luc De Raedt:
Multi-Relational Data Mining: a Workshop Report. SIGKDD Explor. 4(2): 122-124 (2002) - [c54]Luc De Raedt:
Data Mining as Constraint Logic Programming. Computational Logic: Logic Programming and Beyond 2002: 526-547 - [c53]Ulrich Rückert, Stefan Kramer, Luc De Raedt:
Phase Transitions and Stochastic Local Search in k-Term DNF Learning. ECML 2002: 405-417 - [c52]Luc De Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila:
A Theory of Inductive Query Answering. ICDM 2002: 123-130 - [c51]Sau Dan Lee, Luc De Raedt:
Constraint Based Mining of First Order Sequences in SeqLog. KDID 2002: 76-92 - [c50]Kristian Kersting, Tapani Raiko, Luc De Raedt:
Logical Hidden Markov Models (Extendes abstract). Probabilistic Graphical Models 2002 - 2001
- [j20]Saso Dzeroski, Luc De Raedt, Kurt Driessens:
Relational Reinforcement Learning. Mach. Learn. 43(1/2): 7-52 (2001) - [c49]Wim Van Laer, Luc De Raedt:
How to Upgrade Propositional Learners to First Order Logic: A Case Study. Machine Learning and Its Applications 2001: 102-126 - [c48]Stefan Kramer, Luc De Raedt:
Feature Construction with Version Spaces for Biochemical Applications. ICML 2001: 258-265 - [c47]Luc De Raedt, Stefan Kramer:
The Levelwise Version Space Algorithm and its Application to Molecular Fragment Finding. IJCAI 2001: 853-862 - [c46]Kristian Kersting, Luc De Raedt:
Adaptive Bayesian Logic Programs. ILP 2001: 104-117 - [c45]Kristian Kersting, Luc De Raedt:
Towards Combining Inductive Logic Programming with Bayesian Networks. ILP 2001: 118-131 - [c44]Stefan Kramer, Luc De Raedt, Christoph Helma:
Molecular feature mining in HIV data. KDD 2001: 136-143 - [e3]Luc De Raedt, Peter A. Flach:
Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings. Lecture Notes in Computer Science 2167, Springer 2001, ISBN 3-540-42536-5 [contents] - [e2]Luc De Raedt, Arno Siebes:
Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings. Lecture Notes in Computer Science 2168, Springer 2001, ISBN 3-540-42534-9 [contents] - [i3]Kristian Kersting, Luc De Raedt:
Bayesian Logic Programs. CoRR cs.AI/0111058 (2001) - 2000
- [j19]Luc De Raedt, Johannes A. La Poutré, Floor Verdenius:
AI Research in the Benelux - Guest Editorial. AI Commun. 13(1): 1-2 (2000) - [j18]Luc De Raedt, Maarten van Someren:
Benelearn: The First 10 Years. AI Commun. 13(1): 3-4 (2000) - [c43]Kristian Kersting, Luc De Raedt:
Bayesian Logic Programs. ILP Work-in-progress reports 2000 - [c42]Luc De Raedt:
A Logical Database Mining Query Language. ILP 2000: 78-92 - [i2]Hendrik Blockeel, Luc De Raedt, Jan Ramon:
Top-down induction of clustering trees. CoRR cs.LG/0011032 (2000) - [i1]Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart Demoen:
Scaling Up Inductive Logic Programming by Learning from Interpretations. CoRR cs.LG/0011044 (2000)
1990 – 1999
- 1999
- [j17]Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart Demoen:
Scaling Up Inductive Logic Programming by Learning from Interpretations. Data Min. Knowl. Discov. 3(1): 59-93 (1999) - [c41]Shan-Hwei Nienhuys-Cheng, Wim Van Laer, Jan Ramon, Luc De Raedt:
Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms. ILP 1999: 245-256 - [c40]Jan Ramon, Luc De Raedt:
Instance Based Function Learning. ILP 1999: 268-278 - [c39]Luc De Raedt:
Database Mining and Inductive Logic Programming. JFPLC 1999: 73-74 - [c38]Luc De Raedt, Hendrik Blockeel:
Relational Learning and Inductive Logic Programming Made Easy Abstract of Tutorial. PKDD 1999: 590 - [p1]Luc De Raedt:
A Perspective on Inductive Logic Programming. The Logic Programming Paradigm 1999: 335-346 - 1998
- [j16]Hendrik Blockeel, Luc De Raedt:
Isidd: An Interactive System for Inductive Database Design. Appl. Artif. Intell. 12(5): 385-420 (1998) - [j15]Hendrik Blockeel, Luc De Raedt:
Top-Down Induction of First-Order Logical Decision Trees. Artif. Intell. 101(1-2): 285-297 (1998) - [c37]Luc De Raedt:
An Inductive Logic Programming Query Language for Database Mining. AISC 1998: 1-13 - [c36]Nico Jacobs, Kurt Driessens, Luc De Raedt:
Inductive verification and validation of multi agent systems. EUROVAV 1998 - [c35]Hendrik Blockeel, Luc De Raedt, Jan Ramon:
Top-Down Induction of Clustering Trees. ICML 1998: 55-63 - [c34]Saso Dzeroski, Luc De Raedt, Hendrik Blockeel:
Relational Reinforcement Learning. ICML 1998: 136-143 - [c33]Luc De Raedt:
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract). ILP 1998: 1-8 - [c32]Saso Dzeroski, Luc De Raedt, Hendrik Blockeel:
Relational Reinforcement Learning. ILP 1998: 11-22 - [c31]Nico Jacobs, Kurt Driessens, Luc De Raedt:
Using ILP-Systems for Verification and Validation of Multi-agent Systems. ILP 1998: 145-154 - [c30]Kurt Driessens, Nico Jacobs, Nathalie Cossement, Patrick Monsieurs, Luc De Raedt:
Inductive Verification and Validation of the KULRoT RoboCup Team. RoboCup 1998: 193-206 - 1997
- [j14]Luc De Raedt:
Logical Settings for Concept-Learning. Artif. Intell. 95(1): 187-201 (1997) - [j13]Luc De Raedt:
Artificial Intelligence in Belgium and the BeNeLux: Past and Future. AI Commun. 10(3-4): 201-202 (1997) - [j12]Luc De Raedt, Luc Dehaspe:
Clausal Discovery. Mach. Learn. 26(2-3): 99-146 (1997) - [c29]Luc De Raedt, Peter Idestam-Almquist, Gunther Sablon:
Theta-Subsumption for Structural Matching. ECML 1997: 73-84 - [c28]Luc De Raedt:
Machine Learning for Verification and Validation. EUROVAV 1997: 4 - [c27]Hendrik Blockeel, Luc De Raedt:
Lookahead and Discretization in ILP. ILP 1997: 77-84 - [c26]Luc Dehaspe, Luc De Raedt:
Mining Association Rules in Multiple Relations. ILP 1997: 125-132 - [c25]Luc De Raedt, Hendrik Blockeel:
Using Logical Decision Trees for Clustering. ILP 1997: 133-140 - [c24]Wim Van Laer, Luc De Raedt, Saso Dzeroski:
On Multi-class Problems and Discretization in Inductive Logic Programming. ISMIS 1997: 277-286 - 1996
- [j11]Luc De Raedt, Nada Lavrac:
Multiple Predicate Learning in Two Inductive Logic Programming Settings. Log. J. IGPL 4(2): 227-254 (1996) - [c23]Erika Van Baelen, Luc De Raedt:
Analysis and Prediction of Piano Performances Using Inductive Logic Programming. Inductive Logic Programming Workshop 1996: 55-71 - [c22]Hendrik Blockeel, Luc De Raedt:
Relational Knowledge Discovery in Databases. Inductive Logic Programming Workshop 1996: 199-211 - [c21]Hendrik Blockeel, Luc De Raedt:
Inductive Database Design. ISMIS 1996: 376-385 - [c20]Luc De Raedt:
PAC-Learning Logic Programs under the Closed-World Assumption. ISMIS 1996: 531-540 - [c19]Luc Dehaspe, Luc De Raedt:
DLAB: A Declarative Language Bias Formalism. ISMIS 1996: 613-622 - 1995
- [j10]Nada Lavrac, Luc De Raedt:
Inductive Logic Programming: A Survey of European Research. AI Commun. 8(1): 3-19 (1995) - [j9]Hilde Adé, Luc De Raedt, Maurice Bruynooghe:
Declarative Bias for Specific-to-General ILP Systems. Mach. Learn. 20(1-2): 119-154 (1995) - [c18]Luc De Raedt, Wim Van Laer:
Inductive Constraint Logic. ALT 1995: 80-94 - [c17]Gunther Sablon, Luc De Raedt:
Forgetting and Compacting data in Concept Learning. IJCAI 1995: 432-438 - 1994
- [j8]Gunther Sablon, Luc De Raedt, Maurice Bruynooghe:
Iterative Versionspaces. Artif. Intell. 69(1-2): 393-409 (1994) - [j7]Luc De Raedt, Saso Dzeroski:
First-Order jk-Clausal Theories are PAC-Learnable. Artif. Intell. 70(1-2): 375-392 (1994) - [j6]Stephen H. Muggleton, Luc De Raedt:
Inductive Logic Programming: Theory and Methods. J. Log. Program. 19/20: 629-679 (1994) - [c16]Hilde Adé, Bart Malfait, Luc De Raedt:
RUTH: an ILP Theory Revision System. ISMIS 1994: 336-345 - [c15]Wim Van Laer, Luc Dehaspe, Luc De Raedt:
Applications of a Logical Discovery Engine. KDD Workshop 1994: 263-274 - [e1]Francesco Bergadano, Luc De Raedt:
Machine Learning: ECML-94, European Conference on Machine Learning, Catania, Italy, April 6-8, 1994, Proceedings. Lecture Notes in Computer Science 784, Springer 1994, ISBN 3-540-57868-4 [contents] - 1993
- [c14]Luc De Raedt, Nada Lavrac, Saso Dzeroski:
Multiple Predicate Learning. IJCAI 1993: 1037-1043 - [c13]Luc De Raedt, Maurice Bruynooghe:
A Theory of Clausal Discovery. IJCAI 1993: 1058-1063 - [c12]Luc De Raedt, Nada Lavrac:
The Many Faces of Inductive Logic Programming. ISMIS 1993: 435-449 - [c11]Luc De Raedt:
A Brief Introduction to Inductive Logic Programming. ILPS 1993: 45-51 - 1992
- [j5]Luc De Raedt, Maurice Bruynooghe:
Belief Updating from Integrity Constraints and Queries. Artif. Intell. 53(2-3): 291-307 (1992) - [j4]Luc De Raedt, Johan Feyaerts, Maurice Bruynooghe:
Acquiring object-knowledge. J. Exp. Theor. Artif. Intell. 4(3): 213-232 (1992) - [j3]Luc De Raedt, Maurice Bruynooghe:
A unifying framework for concept-learning algorithms. Knowl. Eng. Rev. 7(3): 251-269 (1992) - [j2]Luc De Raedt, Maurice Bruynooghe:
Interactive Concept-Learning and Constructive Induction by Analogy. Mach. Learn. 8: 107-150 (1992) - [c10]Hilde Adé, Luc De Raedt, Maurice Bruynooghe:
Inverse Resolution in an Integrated Inductive-Deductive Learning System. ECAI 1992: 456-457 - 1991
- [c9]Luc De Raedt, Johan Feyaerts, Maurice Bruynooghe:
Acquiring Object-Knowledge for Learning Systems. EWSL 1991: 245-264 - [c8]Luc De Raedt:
Panel: Logic and Learnability. EWSL 1991: 344 - [c7]Luc De Raedt, Maurice Bruynooghe, Bern Martens:
Integrity Constraints and Interactive Concept-Learning. ML 1991: 394-398 - 1990
- [j1]Luc De Raedt, Maurice Bruynooghe:
Indirect relevance and bias in inductive concept-learning. Knowl. Acquis. 2(4): 365-390 (1990) - [c6]Luc De Raedt, Maurice Bruynooghe:
On Negation and Three-Valued Logic in Interactive Concept-Learning. ECAI 1990: 207-212
1980 – 1989
- 1989
- [c5]Gunther Sablon, Luc De Raedt, Maurice Bruynooghe:
Generalizing Multiple Examples in Explanation Based Learning. AII 1989: 177-183 - [c4]Luc De Raedt, Maurice Bruynooghe:
Constructive Induction by Analogy. ML 1989: 476-477 - [c3]Maurice Bruynooghe, Luc De Raedt, Danny De Schreye:
Explanation Based Program Transformation. IJCAI 1989: 407-412 - [c2]Luc De Raedt, Maurice Bruynooghe:
Towards Friendly Concept-Learners. IJCAI 1989: 849-858 - 1988
- [c1]Luc De Raedt, Maurice Bruynooghe:
On Interactive Concept-Learning and Assimilation. EWSL 1988: 167-176
Coauthor Index
[j96] [c218] [j92] [j88] [c208] [i60] [i58] [i57] [j82] [c203] [c198] [p16] [i42] [c187] [c183] [i33] [c175] [i31] [j70] [c172] [j62] [c169] [j59] [j57] [c162] [c155] [i22] [c153] [c152] [c146] [c138] [i17] [p12] [i14] [j44] [j43] [c119] [i13] [c107] [p9] [i10] [c99] [j36] [c94] [c92] [c88] [c84] [c79]
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