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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
[j95]Giuseppe Marra
, Sebastijan Dumancic, Robin Manhaeve, Luc De Raedt:
From statistical relational to neurosymbolic artificial intelligence: A survey. Artif. Intell. 328: 104062 (2024)- [j94]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) - [j93]Victor Verreet, Luc De Raedt, Jessa Bekker:
Modeling PU learning using probabilistic logic programming. Mach. Learn. 113(3): 1351-1372 (2024) - [c218]Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt:
SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge. AAAI 2024: 20123-20133 - [c217]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 - [c216]Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt:
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments. LREC/COLING 2024: 3297-3313 - [c215]Jaron Maene, Vincent Derkinderen, Luc De Raedt:
On the Hardness of Probabilistic Neurosymbolic Learning. ICML 2024 - [c214]Adem Kikaj, Giuseppe Marra, Luc De Raedt:
Subgraph Mining for Graph Neural Networks. IDA (1) 2024: 141-152 - [c213]Ying Jiao, Luc De Raedt, Giuseppe Marra:
Valid Text-to-SQL Generation with Unification-Based DeepStochLog. NeSy (1) 2024: 312-330 - [i65]Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt:
Semirings for Probabilistic and Neuro-Symbolic Logic Programming. CoRR abs/2402.13782 (2024) - [i64]Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt:
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments. CoRR abs/2403.03203 (2024) - [i63]Jaron Maene, Vincent Derkinderen, Luc De Raedt:
On the Hardness of Probabilistic Neurosymbolic Learning. CoRR abs/2406.04472 (2024) - [i62]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) - [i61]Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone:
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic. CoRR abs/2408.08133 (2024) - 2023
- [j92]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from contextual examples for combinatorial optimisation. Artif. Intell. 314: 103794 (2023) - [j91]Pietro Totis, Jesse Davis, Luc De Raedt, Angelika Kimmig:
Lifted Reasoning for Combinatorial Counting. J. Artif. Intell. Res. 76: 1-58 (2023) - [j90]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) - [j89]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) - [j88]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) - [j87]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
- [j86]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) - [j85]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) - [j84]Wen-Chi Yang, Jean-François Raskin, Luc De Raedt:
Lifted model checking for relational MDPs. Mach. Learn. 111(10): 3797-3838 (2022) - [j83]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. 22(1): 81-114 (2022) - [j82]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 [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
- [j81]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
- [j80]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) - [j79]Vaishak Belle, Luc De Raedt:
Semiring programming: A semantic framework for generalized sum product problems. Int. J. Approx. Reason. 126: 181-201 (2020) - [j78]Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt:
Predictive spreadsheet autocompletion with constraints. Mach. Learn. 109(2): 307-325 (2020) - [j77]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
- [j76]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
- [j75]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
- [j74]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) - [j73]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) - [j72]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) - [j71]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) - [j70]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) - [j69]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt:
Algebraic model counting. J. Appl. Log. 22: 46-62 (2017) - [j68]Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt:
Learning constraints in spreadsheets and tabular data. Mach. Learn. 106(9-10): 1441-1468 (2017) - [j67]Sergey Paramonov, Matthijs van Leeuwen, Luc De Raedt:
Relational data factorization. Mach. Learn. 106(12): 1867-1904 (2017) - [j66]Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt:
Planning in hybrid relational MDPs. Mach. Learn. 106(12): 1905-1932 (2017) - [j65]Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: an algebraic Prolog for machine learning. Mach. Learn. 106(12): 1933-1969 (2017) - [j64]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]