Luc De Raedt
182 publications
26 venues
H Index 49
Affiliation
Catholic University of Leuven, BelgiumUniversity of Freiburg, Germany
Links
DBLPGoogle Scholar
ORCID- https://people.cs.kuleuven.be/~luc.deraedt/
- https://zbmath.org/authors/?q=ai:de-raedt.luc
- https://mathscinet.ams.org/mathscinet/MRAuthorID/308460
- https://mathgenealogy.org/id.php?id=125061
- https://www.wikidata.org/entity/Q102340033
- https://id.loc.gov/authorities/names/n94018540
- https://d-nb.info/gnd/123106532
| Name | Venue | Year | citations |
|---|---|---|---|
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs.
|
AAAI | 2026 | 0 |
| Relational Neurosymbolic Markov Models.
|
AAAI | 2025 | 2 |
| The Gradient of Algebraic Model Counting.
|
AAAI | 2025 | 1 |
| Neurosymbolic Reinforcement Learning: Playing MiniHack with Probabilistic Logic Shields.
|
AAAI | 2025 | 2 |
| Queryable and Interpretable PU Learning Through Probabilistic Circuits.
|
ECML/PKDD | 2025 | 0 |
| TABIA: A Language to Transform, Combine, and Reason over Semi-Structured Tabular Data.
|
ECAI | 2025 | 0 |
| Correction to: Modeling PU learning using probabilistic logic programming.
|
MLJ | 2025 | 0 |
| Neurosymbolic OCR for Handwritten Tax Forms.
|
ECAI | 2025 | 0 |
| DEEPGRAPHLOG for Layered Neurosymbolic AI.
|
ECAI | 2025 | 2 |
| NeSyA: Neurosymbolic Automata.
|
IJCAI | 2025 | 0 |
| On the Hardness of Probabilistic Neurosymbolic Learning.
|
ICML | 2024 | 7 |
| Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation.
|
AAAI | 2024 | 0 |
| EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic.
|
ECAI | 2024 | 3 |
| Automated Reasoning in Systems Biology: A Necessity for Precision Medicine.
|
KR | 2024 | 0 |
| SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge.
|
AAAI | 2024 | 0 |
| Modeling PU learning using probabilistic logic programming.
|
MLJ | 2024 | 0 |
| Declarative probabilistic logic programming in discrete-continuous domains.
|
Artificial Intelligence | 2024 | 0 |
| From statistical relational to neurosymbolic artificial intelligence: A survey.
|
Artificial Intelligence | 2024 | 0 |
| Lifted Reasoning for Combinatorial Counting.
|
JAIR | 2023 | 0 |
| Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach.
|
ECML/PKDD | 2023 | 15 |
| A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer.
|
JAIR | 2023 | 14 |
| Safe Reinforcement Learning via Probabilistic Logic Shields.
|
IJCAI | 2023 | 51 |
| Neural probabilistic logic programming in discrete-continuous domains.
|
UAI | 2023 | 31 |
| Soft-Unification in Deep Probabilistic Logic.
|
NIPS/NeurIPS | 2023 | 17 |
| First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs.
|
JAIR | 2023 | 0 |
| Learning MAX-SAT from contextual examples for combinatorial optimisation.
|
Artificial Intelligence | 2023 | 0 |
| Inference and Learning with Model Uncertainty in Probabilistic Logic Programs.
|
AAAI | 2022 | 3 |
| DeepStochLog: Neural Stochastic Logic Programming.
|
AAAI | 2022 | 0 |
| Lifted model checking for relational MDPs.
|
MLJ | 2022 | 0 |
| Approximate Inference for Neural Probabilistic Logic Programming.
|
KR | 2021 | 31 |
| Learning CNF Theories Using MDL and Predicate Invention.
|
IJCAI | 2021 | 6 |
| Mapping probability word problems to executable representations.
|
EMNLP | 2021 | 7 |
| Democratizing Constraint Satisfaction Problems through Machine Learning.
|
AAAI | 2021 | 2 |
| Neural probabilistic logic programming in DeepProbLog.
|
Artificial Intelligence | 2021 | 0 |
| Algebraic Circuits for Decision Theoretic Inference and Learning.
|
ECAI | 2020 | 11 |
| Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation.
|
AAAI | 2020 | 17 |
| ProbAnch: a Modular Probabilistic Anchoring Framework.
|
IJCAI | 2020 | 2 |
| From Statistical Relational to Neuro-Symbolic Artificial Intelligence.
|
IJCAI | 2020 | 131 |
| VisualSynth: Democratizing Data Science in Spreadsheets.
|
ECML/PKDD | 2020 | 0 |
| Ordering Variables for Weighted Model Integration.
|
UAI | 2020 | 4 |
| Predictive spreadsheet autocompletion with constraints.
|
MLJ | 2020 | 0 |
| Acquiring Integer Programs from Data.
|
IJCAI | 2019 | 17 |
| How to Exploit Structure while Solving Weighted Model Integration Problems.
|
UAI | 2019 | 18 |
| The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration.
|
IJCAI | 2019 | 16 |
| Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation.
|
AAAI | 2019 | 44 |
| Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach.
|
Autonomous Robots | 2019 | 0 |
| DeepProbLog: Neural Probabilistic Logic Programming.
|
NIPS/NeurIPS | 2018 | 689 |
| Learning Constraints From Examples.
|
AAAI | 2018 | 83 |
| Learning SMT(LRA) Constraints using SMT Solvers.
|
IJCAI | 2018 | 51 |
| Relational affordances for multiple-object manipulation.
|
Autonomous Robots | 2018 | 0 |
| Stochastic Constraint Programming with And-Or Branch-and-Bound.
|
IJCAI | 2017 | 9 |
| Planning in hybrid relational MDPs.
|
MLJ | 2017 | 10 |
| Relational data factorization.
|
MLJ | 2017 | 0 |
| Learning constraints in spreadsheets and tabular data.
|
MLJ | 2017 | 36 |
| Solving Probability Problems in Natural Language.
|
IJCAI | 2017 | 21 |
| Relational Affordance Learning for Task-Dependent Robot Grasping.
|
ILP | 2017 | 3 |
| MiningZinc: A declarative framework for constraint-based mining.
|
Artificial Intelligence | 2017 | 41 |
| kProbLog: an algebraic Prolog for machine learning.
|
MLJ | 2017 | 8 |
| TaCLe: Learning Constraints in Tabular Data.
|
CIKM | 2017 | 11 |
| Flexible constrained sampling with guarantees for pattern mining.
|
DMKD | 2017 | 0 |
| Probabilistic logic programming for hybrid relational domains.
|
MLJ | 2016 | 46 |
| Learning the Structure of Dynamic Hybrid Relational Models.
|
ECAI | 2016 | 14 |
| Exploiting local and repeated structure in Dynamic Bayesian Networks.
|
Artificial Intelligence | 2016 | 31 |
| ProbLog2: Probabilistic Logic Programming.
|
ECML/PKDD | 2015 | 60 |
| Probabilistic (logic) programming concepts.
|
MLJ | 2015 | 225 |
| Inducing Probabilistic Relational Rules from Probabilistic Examples.
|
IJCAI | 2015 | 80 |
| kProbLog: An Algebraic Prolog for Kernel Programming.
|
ILP | 2015 | 2 |
| Relational Kernel-Based Grasping with Numerical Features.
|
ILP | 2015 | 4 |
| An Exercise in Declarative Modeling for Relational Query Mining.
|
ILP | 2015 | 8 |
| Anytime Inference in Probabilistic Logic Programs with Tp-Compilation.
|
IJCAI | 2015 | 43 |
| Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming.
|
ECML/PKDD | 2015 | 23 |
| Languages for Learning and Mining.
|
AAAI | 2015 | 7 |
| Graph Invariant Kernels.
|
IJCAI | 2015 | 128 |
| Rank Matrix Factorisation.
|
PAKDD | 2015 | 4 |
| kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract).
|
IJCAI | 2015 | 0 |
| Relational object tracking and learning.
|
ICRA | 2014 | 27 |
| Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics.
|
AAAI | 2014 | 20 |
| Relational Regularization and Feature Ranking.
|
SDM | 2014 | 2 |
| Distributional Clauses Particle Filter.
|
ECML/PKDD | 2014 | 2 |
| Condition Monitoring with Incomplete Observations.
|
ECAI | 2014 | 2 |
| Ranked Tiling.
|
ECML/PKDD | 2014 | 21 |
| PageRank, ProPPR, and Stochastic Logic Programs.
|
ILP | 2014 | 2 |
| Learning relational affordance models for two-arm robots.
|
IROS | 2014 | 11 |
| Occluded object search by relational affordances.
|
ICRA | 2014 | 34 |
| kLog: A language for logical and relational learning with kernels.
|
Artificial Intelligence | 2014 | 0 |
| A particle filter for hybrid relational domains.
|
IROS | 2013 | 46 |
| Allocentric Pose Estimation.
|
ICCV | 2013 | 9 |
| MiningZinc: A Modeling Language for Constraint-Based Mining.
|
IJCAI | 2013 | 0 |
| Declarative Modeling for Machine Learning and Data Mining.
|
ECML/PKDD | 2012 | 20 |
| Learning relational affordance models for robots in multi-object manipulation tasks.
|
ICRA | 2012 | 138 |
| ILP turns 20 - Biography and future challenges.
|
MLJ | 2012 | 86 |
| Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework.
|
PAKDD | 2011 | 10 |
| Itemset mining: A constraint programming perspective.
|
Artificial Intelligence | 2011 | 188 |
| Inference in Probabilistic Logic Programs using Weighted CNF's.
|
UAI | 2011 | 105 |
| Relational Learning for Spatial Relation Extraction from Natural Language.
|
ILP | 2011 | 30 |
| Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection.
|
ILP | 2011 | 20 |
| Learning the Parameters of Probabilistic Logic Programs from Interpretations.
|
ECML/PKDD | 2011 | 82 |
| An Algebraic Prolog for Reasoning about Possible Worlds.
|
AAAI | 2011 | 67 |
| Lifted Probabilistic Inference by First-Order Knowledge Compilation.
|
IJCAI | 2011 | 0 |
| Effective feature construction by maximum common subgraph sampling.
|
MLJ | 2011 | 0 |
| Stochastic relational processes: Efficient inference and applications.
|
MLJ | 2011 | 0 |
| Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning.
|
MLJ | 2011 | 0 |
| Fast learning of relational kernels.
|
MLJ | 2010 | 43 |
| Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses.
|
ILP | 2010 | 2 |
| ProbLog Technology for Inference in a Probabilistic First Order Logic.
|
ECAI | 2010 | 20 |
| Constraint Programming for Data Mining and Machine Learning.
|
AAAI | 2010 | 72 |
| Extending ProbLog with Continuous Distributions.
|
ILP | 2010 | 60 |
| DTProbLog: A Decision-Theoretic Probabilistic Prolog.
|
AAAI | 2010 | 74 |
| Probabilistic Rule Learning.
|
ILP | 2010 | 48 |
| A query language for analyzing networks.
|
CIKM | 2009 | 53 |
| Local Query Mining in a Probabilistic Prolog.
|
IJCAI | 2009 | 10 |
| Grammar Mining.
|
SDM | 2009 | 0 |
| Probabilistic Logic Learning - A Tutorial Abstract.
|
ICLP | 2009 | 0 |
| Correlated itemset mining in ROC space: a constraint programming approach.
|
KDD | 2009 | 83 |
| Towards Clausal Discovery for Stream Mining.
|
ILP | 2009 | 10 |
| Cluster-grouping: from subgroup discovery to clustering.
|
MLJ | 2009 | 0 |
| An experimental evaluation of simplicity in rule learning.
|
Artificial Intelligence | 2008 | 20 |
| Constraint programming for itemset mining.
|
KDD | 2008 | 171 |
| A Simple Model for Sequences of Relational State Descriptions.
|
ECML/PKDD | 2008 | 29 |
| Compressing probabilistic Prolog programs.
|
MLJ | 2008 | 44 |
| Parameter Learning in Probabilistic Databases: A Least Squares Approach.
|
ECML/PKDD | 2008 | 73 |
| On the Efficient Execution of ProbLog Programs.
|
ICLP | 2008 | 66 |
| Integrating Naïve Bayes and FOIL.
|
JMLR | 2007 | 72 |
| Predicting Spike Activity in Neuronal Cultures.
|
IJCNN | 2007 | 1 |
| Probabilistic Explanation Based Learning.
|
ECML/PKDD | 2007 | 25 |
| ProbLog: A Probabilistic Prolog and Its Application in Link Discovery.
|
IJCAI | 2007 | 273 |
| Constraint-Based Pattern Set Mining.
|
SDM | 2007 | 122 |
| On Mining Closed Sets in Multi-Relational Data.
|
IJCAI | 2007 | 27 |
| r-grams: Relational Grams.
|
IJCAI | 2007 | 9 |
| Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting.
|
JMLR | 2006 | 42 |
| kFOIL: Learning Simple Relational Kernels.
|
AAAI | 2006 | 115 |
| Learning Relational Navigation Policies.
|
IROS | 2006 | 36 |
| Don't Be Afraid of Simpler Patterns.
|
ECML/PKDD | 2006 | 66 |
| Revising Probabilistic Prolog Programs.
|
ILP | 2006 | 0 |
| Frequent Hypergraph Mining.
|
ILP | 2006 | 0 |
| Logical Hidden Markov Models.
|
JAIR | 2006 | 0 |
| Towards Learning Stochastic Logic Programs from Proof-Banks.
|
AAAI | 2005 | 16 |
| nFOIL: Integrating Naïve Bayes and FOIL.
|
AAAI | 2005 | 104 |
| Statistical Relational Learning: An Inductive Logic Programming Perspective.
|
ECML/PKDD | 2005 | 5 |
| Towards Optimizing Conjunctive Inductive Queries.
|
PAKDD | 2004 | 9 |
| Condensed Representations for Inductive Logic Programming.
|
KR | 2004 | 52 |
| Cluster-Grouping: From Subgroup Discovery to Clustering.
|
ECML/PKDD | 2004 | 48 |
| Logical Markov Decision Programs and the Convergence of Logical TD(lambda).
|
ILP | 2004 | 33 |
| Bellman goes relational.
|
ICML | 2004 | 123 |
| An Algebra for Inductive Query Evaluation.
|
ICDM | 2003 | 30 |
| Phase Transitions and Stochastic Local Search in k-Term DNF Learning.
|
ECML/PKDD | 2002 | 38 |
| A Theory of Inductive Query Answering.
|
ICDM | 2002 | 111 |
| Relational Reinforcement Learning.
|
MLJ | 2001 | 153 |
| The Levelwise Version Space Algorithm and its Application to Molecular Fragment Finding.
|
IJCAI | 2001 | 167 |
| Feature Construction with Version Spaces for Biochemical Applications.
|
ICML | 2001 | 108 |
| Molecular feature mining in HIV data.
|
KDD | 2001 | 261 |
| Adaptive Bayesian Logic Programs.
|
ILP | 2001 | 122 |
| Towards Combining Inductive Logic Programming with Bayesian Networks.
|
ILP | 2001 | 161 |
| A Logical Database Mining Query Language.
|
ILP | 2000 | 26 |
| Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms.
|
ILP | 1999 | 14 |
| Instance Based Function Learning.
|
ILP | 1999 | 9 |
| Scaling Up Inductive Logic Programming by Learning from Interpretations.
|
DMKD | 1999 | 111 |
| Relational Learning and Inductive Logic Programming Made Easy Abstract of Tutorial.
|
ECML/PKDD | 1999 | 6 |
| Using ILP-Systems for Verification and Validation of Multi-agent Systems.
|
ILP | 1998 | 7 |
| Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract).
|
ILP | 1998 | 138 |
| Top-Down Induction of First-Order Logical Decision Trees.
|
Artificial Intelligence | 1998 | 34 |
| Relational Reinforcement Learning.
|
ICML | 1998 | 10 |
| Top-Down Induction of Clustering Trees.
|
ICML | 1998 | 516 |
| Relational Reinforcement Learning.
|
ILP | 1998 | 0 |
| Mining Association Rules in Multiple Relations.
|
ILP | 1997 | 240 |
| Theta-Subsumption for Structural Matching.
|
ECML/PKDD | 1997 | 7 |
| Using Logical Decision Trees for Clustering.
|
ILP | 1997 | 63 |
| Lookahead and Discretization in ILP.
|
ILP | 1997 | 75 |
| Logical Settings for Concept-Learning.
|
Artificial Intelligence | 1997 | 186 |
| Clausal Discovery.
|
MLJ | 1997 | 224 |
| Declarative Bias for Specific-to-General ILP Systems.
|
MLJ | 1995 | 11 |
| Forgetting and Compacting data in Concept Learning.
|
IJCAI | 1995 | 3 |
| First-Order jk-Clausal Theories are PAC-Learnable.
|
Artificial Intelligence | 1994 | 193 |
| Iterative Versionspaces.
|
Artificial Intelligence | 1994 | 19 |
| A Theory of Clausal Discovery.
|
IJCAI | 1993 | 162 |
| Multiple Predicate Learning.
|
IJCAI | 1993 | 105 |
| Interactive Concept-Learning and Constructive Induction by Analogy.
|
MLJ | 1992 | 41 |
| Inverse Resolution in an Integrated Inductive-Deductive Learning System.
|
ECAI | 1992 | 2 |
| Belief Updating from Integrity Constraints and Queries.
|
Artificial Intelligence | 1992 | 42 |
| On Negation and Three-Valued Logic in Interactive Concept-Learning.
|
ECAI | 1990 | 29 |
| Explanation Based Program Transformation.
|
IJCAI | 1989 | 18 |
| Towards Friendly Concept-Learners.
|
IJCAI | 1989 | 37 |