On Interactive Concept-Learning and Assimilation. - Luc De Raedt; Maurice Bruynooghe (EWSL, 1988)

Generalizing Multiple Examples in Explanation Based Learning. - Gunther Sablon; Luc De Raedt; Maurice Bruynooghe (AII, 1989)

Constructive Induction by Analogy. - Luc De Raedt; Maurice Bruynooghe (ML, 1989)

Explanation Based Program Transformation. - Maurice Bruynooghe; Luc De Raedt; Danny De Schreye (IJCAI, 1989)

Towards Friendly Concept-Learners. - Luc De Raedt; Maurice Bruynooghe (IJCAI, 1989)

On Negation and Three-Valued Logic in Interactive Concept-Learning. - Luc De Raedt; Maurice Bruynooghe (ECAI, 1990)

Indirect relevance and bias in inductive concept-learning. - Luc De Raedt; Maurice Bruynooghe (Knowl. Acquis., 1990)

Panel: Logic and Learnability. - Luc De Raedt (EWSL, 1991)

Acquiring Object-Knowledge for Learning Systems. - Luc De Raedt; Johan Feyaerts; Maurice Bruynooghe (EWSL, 1991)

Integrity Constraints and Interactive Concept-Learning. - Luc De Raedt; Maurice Bruynooghe; Bern Martens (ML, 1991)

Inverse Resolution in an Integrated Inductive-Deductive Learning System. - Hilde Adé; Luc De Raedt; Maurice Bruynooghe (ECAI, 1992)

Belief Updating from Integrity Constraints and Queries. - Luc De Raedt; Maurice Bruynooghe (Artif. Intell., 1992)

Acquiring object-knowledge. - Luc De Raedt; Johan Feyaerts; Maurice Bruynooghe (J. Exp. Theor. Artif. Intell., 1992)

A unifying framework for concept-learning algorithms. - Luc De Raedt; Maurice Bruynooghe (Knowl. Eng. Rev., 1992)

Interactive Concept-Learning and Constructive Induction by Analogy. - Luc De Raedt; Maurice Bruynooghe (Mach. Learn., 1992)

A Theory of Clausal Discovery. - Luc De Raedt; Maurice Bruynooghe (IJCAI, 1993)

Multiple Predicate Learning. - Luc De Raedt; Nada Lavrac; Saso Dzeroski (IJCAI, 1993)

The Many Faces of Inductive Logic Programming. - Luc De Raedt; Nada Lavrac (ISMIS, 1993)

A Brief Introduction to Inductive Logic Programming. - Luc De Raedt (ILPS, 1993)

RUTH: an ILP Theory Revision System. - Hilde Adé; Bart Malfait; Luc De Raedt (ISMIS, 1994)

Applications of a Logical Discovery Engine. - Wim Van Laer; Luc Dehaspe; Luc De Raedt (KDD Workshop, 1994)

First-Order jk-Clausal Theories are PAC-Learnable. - Luc De Raedt; Saso Dzeroski (Artif. Intell., 1994)

Iterative Versionspaces. - Gunther Sablon; Luc De Raedt; Maurice Bruynooghe (Artif. Intell., 1994)

Inductive Logic Programming: Theory and Methods. - Stephen H. Muggleton; Luc De Raedt (J. Log. Program., 1994)

Inductive Constraint Logic. - Luc De Raedt; Wim Van Laer (ALT, 1995)

Forgetting and Compacting data in Concept Learning. - Gunther Sablon; Luc De Raedt (IJCAI, 1995)

Inductive Logic Programming: A Survey of European Research. - Nada Lavrac; Luc De Raedt (AI Commun., 1995)

Declarative Bias for Specific-to-General ILP Systems. - Hilde Adé; Luc De Raedt; Maurice Bruynooghe (Mach. Learn., 1995)

Analysis and Prediction of Piano Performances Using Inductive Logic Programming. - Erika Van Baelen; Luc De Raedt (Inductive Logic Programming Workshop, 1996)

Relational Knowledge Discovery in Databases. - Hendrik Blockeel; Luc De Raedt (Inductive Logic Programming Workshop, 1996)

Inductive Database Design. - Hendrik Blockeel; Luc De Raedt (ISMIS, 1996)

DLAB: A Declarative Language Bias Formalism. - Luc Dehaspe; Luc De Raedt (ISMIS, 1996)

PAC-Learning Logic Programs under the Closed-World Assumption. - Luc De Raedt (ISMIS, 1996)

Multiple Predicate Learning in Two Inductive Logic Programming Settings. - Luc De Raedt; Nada Lavrac (Log. J. IGPL, 1996)

Theta-Subsumption for Structural Matching. - Luc De Raedt; Peter Idestam-Almquist; Gunther Sablon (ECML, 1997)

Machine Learning for Verification and Validation. - Luc De Raedt (EUROVAV, 1997)

Lookahead and Discretization in ILP. - Hendrik Blockeel; Luc De Raedt (ILP, 1997)

Mining Association Rules in Multiple Relations. - Luc Dehaspe; Luc De Raedt (ILP, 1997)

Using Logical Decision Trees for Clustering. - Luc De Raedt; Hendrik Blockeel (ILP, 1997)

On Multi-class Problems and Discretization in Inductive Logic Programming. - Wim Van Laer; Luc De Raedt; Saso Dzeroski (ISMIS, 1997)

Logical Settings for Concept-Learning. - Luc De Raedt (Artif. Intell., 1997)

Artificial Intelligence in Belgium and the BeNeLux: Past and Future. - Luc De Raedt (AI Commun., 1997)

Clausal Discovery. - Luc De Raedt; Luc Dehaspe (Mach. Learn., 1997)

An Inductive Logic Programming Query Language for Database Mining. - Luc De Raedt (AISC, 1998)

Inductive verification and validation of multi agent systems. - Nico Jacobs; Kurt Driessens; Luc De Raedt (EUROVAV, 1998)

Top-Down Induction of Clustering Trees. - Hendrik Blockeel; Luc De Raedt; Jan Ramon (ICML, 1998)

Relational Reinforcement Learning. - Saso Dzeroski; Luc De Raedt; Hendrik Blockeel (ICML, 1998)

Using ILP-Systems for Verification and Validation of Multi-agent Systems. - Nico Jacobs; Kurt Driessens; Luc De Raedt (ILP, 1998)

Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract). - Luc De Raedt (ILP, 1998)

Inductive Verification and Validation of the KULRoT RoboCup Team. - Kurt Driessens; Nico Jacobs; Nathalie Cossement; Patrick Monsieurs; Luc De Raedt (RoboCup, 1998)

Isidd: An Interactive System for Inductive Database Design. - Hendrik Blockeel; Luc De Raedt (Appl. Artif. Intell., 1998)

Top-Down Induction of First-Order Logical Decision Trees. - Hendrik Blockeel; Luc De Raedt (Artif. Intell., 1998)

A Perspective on Inductive Logic Programming. - Luc De Raedt (The Logic Programming Paradigm, 1999)

Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms. - Shan-Hwei Nienhuys-Cheng; Wim Van Laer; Jan Ramon; Luc De Raedt (ILP, 1999)

Instance Based Function Learning. - Jan Ramon; Luc De Raedt (ILP, 1999)

Database Mining and Inductive Logic Programming. - Luc De Raedt (JFPLC, 1999)

Relational Learning and Inductive Logic Programming Made Easy Abstract of Tutorial. - Luc De Raedt; Hendrik Blockeel (PKDD, 1999)

Scaling Up Inductive Logic Programming by Learning from Interpretations. - Hendrik Blockeel; Luc De Raedt; Nico Jacobs; Bart Demoen (Data Min. Knowl. Discov., 1999)

Bayesian Logic Programs. - Kristian Kersting; Luc De Raedt (ILP Work-in-progress reports, 2000)

A Logical Database Mining Query Language. - Luc De Raedt (ILP, 2000)

AI Research in the Benelux - Guest Editorial. - Luc De Raedt; Johannes A. La Poutré; Floor Verdenius (AI Commun., 2000)

Benelearn: The First 10 Years. - Luc De Raedt; Maarten van Someren (AI Commun., 2000)

How to Upgrade Propositional Learners to First Order Logic: A Case Study. - Wim Van Laer; Luc De Raedt (Machine Learning and Its Applications, 2001)

Feature Construction with Version Spaces for Biochemical Applications. - Stefan Kramer 001; Luc De Raedt (ICML, 2001)

The Levelwise Version Space Algorithm and its Application to Molecular Fragment Finding. - Luc De Raedt; Stefan Kramer 001 (IJCAI, 2001)

Adaptive Bayesian Logic Programs. - Kristian Kersting; Luc De Raedt (ILP, 2001)

Towards Combining Inductive Logic Programming with Bayesian Networks. - Kristian Kersting; Luc De Raedt (ILP, 2001)

Molecular feature mining in HIV data. - Stefan Kramer 001; Luc De Raedt; Christoph Helma (KDD, 2001)

Relational Reinforcement Learning. - Saso Dzeroski; Luc De Raedt; Kurt Driessens (Mach. Learn., 2001)

Data Mining as Constraint Logic Programming. - Luc De Raedt (Computational Logic: Logic Programming and Beyond, 2002)

Phase Transitions and Stochastic Local Search in k-Term DNF Learning. - Ulrich Rückert 002; Stefan Kramer 001; Luc De Raedt (ECML, 2002)

A Theory of Inductive Query Answering. - Luc De Raedt; Manfred Jaeger; Sau Dan Lee; Heikki Mannila (ICDM, 2002)

Constraint Based Mining of First Order Sequences in SeqLog. - Sau Dan Lee; Luc De Raedt (KDID, 2002)

Logical Hidden Markov Models (Extendes abstract). - Kristian Kersting; Tapani Raiko; Luc De Raedt (Probabilistic Graphical Models, 2002)

Multi-Relational Data Mining: a Workshop Report. - Saso Dzeroski; Luc De Raedt (SIGKDD Explor., 2002)

A Perspective on Inductive Databases. - Luc De Raedt (SIGKDD Explor., 2002)

Towards Optimizing Conjunctive Inductive Queries. - Johannes Fischer 001; Luc De Raedt (KDID, 2003)

An Algebra for Inductive Query Evaluation. - Sau Dan Lee; Luc De Raedt (KDID, 2003)

Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models. - Kristian Kersting; Tapani Raiko; Stefan Kramer 001; Luc De Raedt (Pacific Symposium on Biocomputing, 2003)

Multi-relational data mining: the current frontiers. - Saso Dzeroski; Luc De Raedt (SIGKDD Explor., 2003)

Multirelational data mining 2003: workshop report. - Saso Dzeroski; Luc De Raedt; Stefan Wrobel (SIGKDD Explor., 2003)

Probabilistic logic learning. - Luc De Raedt; Kristian Kersting (SIGKDD Explor., 2003)

Probabilistic Inductive Logic Programming. - Luc De Raedt; Kristian Kersting (ALT, 2004)

Constraint Based Mining of First Order Sequences in SeqLog. - Sau Dan Lee; Luc De Raedt (Database Support for Data Mining Applications, 2004)

Towards Query Evaluation in Inductive Databases Using Version Spaces. - Luc De Raedt (Database Support for Data Mining Applications, 2004)

Inductive Querying for Discovering Subgroups and Clusters. - Albrecht Zimmermann; Luc De Raedt (Constraint-Based Mining and Inductive Databases, 2004)

Predictive Graph Mining. - Andreas Karwath; Luc De Raedt (Discovery Science, 2004)

CorClass: Correlated Association Rule Mining for Classification. - Albrecht Zimmermann; Luc De Raedt (Discovery Science, 2004)

Bellman goes relational. - Kristian Kersting; Martijn van Otterlo; Luc De Raedt (ICML, 2004)

Logical Markov Decision Programs and the Convergence of Logical TD(lambda). - Kristian Kersting; Luc De Raedt (ILP, 2004)

An Efficient Algorithm for Mining String Databases Under Constraints. - Sau Dan Lee; Luc De Raedt (KDID, 2004)

Condensed Representations for Inductive Logic Programming. - Luc De Raedt; Jan Ramon (KR, 2004)

Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds. - Christoph Helma; Tobias Cramer; Stefan Kramer 001; Luc De Raedt (J. Chem. Inf. Model., 2004)

nFOIL: Integrating Naïve Bayes and FOIL. - Niels Landwehr; Kristian Kersting; Luc De Raedt (AAAI, 2005)

Towards Learning Stochastic Logic Programs from Proof-Banks. - Luc De Raedt; Kristian Kersting; Sunna Torge 001 (AAAI, 2005)

05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning, 2005)

05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning, 2005)

CLASSIC'CL: An Integrated ILP System. - Christian Stolle; Andreas Karwath; Luc De Raedt (Discovery Science, 2005)

Statistical Relational Learning: An Inductive Logic Programming Perspective. - Luc De Raedt (PKDD, 2005)

Advances in Mining Graphs, Trees and Sequences. - Takashi Washio; Luc De Raedt; Joost N. Kok (Fundam. Informaticae, 2005)

kFOIL: Learning Simple Relational Kernels. - Niels Landwehr; Andrea Passerini; Luc De Raedt; Paolo Frasconi (AAAI, 2006)

Frequent Hypergraph Mining. - Tamás Horváth 001; Björn Bringmann; Luc De Raedt (ILP, 2006)

Revising Probabilistic Prolog Programs. - Luc De Raedt; Kristian Kersting; Angelika Kimmig; Kate Revoredo; Hannu Toivonen (ILP, 2006)

Mining Bi-sets in Numerical Data. - Jérémy Besson; Céline Robardet; Luc De Raedt; Jean-François Boulicaut (KDID, 2006)

IQL: A Proposal for an Inductive Query Language. - Siegfried Nijssen; Luc De Raedt (KDID, 2006)

Don't Be Afraid of Simpler Patterns. - Björn Bringmann; Albrecht Zimmermann; Luc De Raedt; Siegfried Nijssen (PKDD, 2006)

Logical Hidden Markov Models. - Kristian Kersting; Luc De Raedt; Tapani Raiko (J. Artif. Intell. Res., 2006)

SMIREP: Predicting Chemical Activity from SMILES. - Andreas Karwath; Luc De Raedt (J. Chem. Inf. Model., 2006)

Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. - Andrea Passerini; Paolo Frasconi; Luc De Raedt (J. Mach. Learn. Res., 2006)

Learning Relational Navigation Policies. - Alexandru Cocora; Kristian Kersting; Wolfram Burgard; Luc De Raedt; Christian Plagemann (Künstliche Intell., 2006)

A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. - 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; et al. (Prax. Inf.verarb. Kommun., 2006)

07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Kristian Kersting; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007)

Probabilistic Explanation Based Learning. - Angelika Kimmig; Luc De Raedt; Hannu Toivonen (ECML, 2007)

On Mining Closed Sets in Multi-Relational Data. - Gemma C. Garriga; Roni Khardon; Luc De Raedt (IJCAI, 2007)

r-grams: Relational Grams. - Niels Landwehr; Luc De Raedt (IJCAI, 2007)

Statistical Relational Learning - A Logical Approach (Abstract of Invited Talk). - Luc De Raedt (NeSy, 2007)

ProbLog: A Probabilistic Prolog and Its Application in Link Discovery. - Luc De Raedt; Angelika Kimmig; Hannu Toivonen (IJCAI, 2007)

Predicting Spike Activity in Neuronal Cultures. - Tayfun Gürel; Ulrich Egert; Steffen Kandler; Luc De Raedt; Stefan Rotter (IJCNN, 2007)

ProbLog and its Application to Link Mining in Biological Networks. - Luc De Raedt (MLG, 2007)

Constraint-Based Pattern Set Mining. - Luc De Raedt; Albrecht Zimmermann (SDM, 2007)

Learning to transfer optimal navigation policies. - Kristian Kersting; Christian Plagemann; Alexandru Cocora; Wolfram Burgard; Luc De Raedt (Adv. Robotics, 2007)

Ranking neurons for mining structure-activity relations in biological neural networks: NeuronRank. - Tayfun Gürel; Luc De Raedt; Stefan Rotter (Neurocomputing, 2007)

Integrating Naïve Bayes and FOIL. - Niels Landwehr; Kristian Kersting; Luc De Raedt (J. Mach. Learn. Res., 2007)

Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank. - Tayfun Gürel; Luc De Raedt; Stefan Rotter (Perspectives of Neural-Symbolic Integration, 2007)

Logical and relational learning. - Luc De Raedt (Springer, 2008)

08041 Summary – Recurrent Neural Networks - Models, Capacities, and Applications. - Luc De Raedt; Barbara Hammer; Pascal Hitzler; Wolfgang Maass 001 (Recurrent Neural Networks, 2008)

08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications. - Luc De Raedt; Barbara Hammer; Pascal Hitzler; Wolfgang Maass 001 (Recurrent Neural Networks, 2008)

Active Learning for High Throughput Screening. - Kurt De Grave; Jan Ramon; Luc De Raedt (Discovery Science, 2008)

On the Efficient Execution of ProbLog Programs. - Angelika Kimmig; Vítor Santos Costa; Ricardo Rocha 001; Bart Demoen; Luc De Raedt (ICLP, 2008)

Basic Principles of Learning Bayesian Logic Programs. - Kristian Kersting; Luc De Raedt (Probabilistic Inductive Logic Programming, 2008)

Relational Sequence Learning. - Kristian Kersting; Luc De Raedt; Bernd Gutmann; Andreas Karwath; Niels Landwehr (Probabilistic Inductive Logic Programming, 2008)

Probabilistic Inductive Logic Programming. - Luc De Raedt; Kristian Kersting (Probabilistic Inductive Logic Programming, 2008)

Constraint programming for itemset mining. - Luc De Raedt; Tias Guns; Siegfried Nijssen (KDD, 2008)

Parameter Learning in Probabilistic Databases: A Least Squares Approach. - Bernd Gutmann; Angelika Kimmig; Kristian Kersting; Luc De Raedt (ECML/PKDD (1), 2008)

A Simple Model for Sequences of Relational State Descriptions. - Ingo Thon; Niels Landwehr; Luc De Raedt (ECML/PKDD (2), 2008)

Logical and Relational Learning. - Luc De Raedt (SBIA, 2008)

Logic, Probability and Learning, or an Introduction to Statistical Relational Learning. - Luc De Raedt (SBIA, 2008)

An experimental evaluation of simplicity in rule learning. - Ulrich Rückert 002; Luc De Raedt (Artif. Intell., 2008)

Relational Transformation-based Tagging for Activity Recognition. - Niels Landwehr; Bernd Gutmann; Ingo Thon; Luc De Raedt; Matthai Philipose (Fundam. Informaticae, 2008)

Compressing probabilistic Prolog programs. - Luc De Raedt; Kristian Kersting; Angelika Kimmig; Kate Revoredo; Hannu Toivonen (Mach. Learn., 2008)

A query language for analyzing networks. - Anton Dries; Siegfried Nijssen; Luc De Raedt (CIKM, 2009)

Constraint Programming for Data Mining. - Luc De Raedt (EGC, 2009)

The Logic of Learning. - Luc De Raedt (ICFCA, 2009)

Probabilistic Logic Learning - A Tutorial Abstract. - Luc De Raedt (ICLP, 2009)

Local Query Mining in a Probabilistic Prolog. - Angelika Kimmig; Luc De Raedt (IJCAI, 2009)

Towards Clausal Discovery for Stream Mining. - Anton Dries; Luc De Raedt (ILP, 2009)

Correlated itemset mining in ROC space: a constraint programming approach. - Siegfried Nijssen; Tias Guns; Luc De Raedt (KDD, 2009)

An Exercise with Statistical Relational Learning Systems. - Maurice Bruynooghe; Daan Fierens; Bernd Gutmann; Angelika Kimmig; Niels Landwehr; Wannes Meert; Ingo Thon; Luc De Raedt (MLG/SRL@ILP, 2009)

Maximum Common Subgraph Mining: A Fast and Effective Approach towards Feature Generation. - Leander Schietgat; Fabrizio Costa; Jan Ramon; Luc De Raedt (MLG/SRL@ILP, 2009)

From ProbLog to First Order Logic: A First Exploration. - Joost Vennekens; Angelika Kimmig; Theofrastos Mantadelis; Bernd Gutmann; Luc De Raedt; Maurice Bruynooghe (MLG/SRL@ILP, 2009)

Grammar Mining. - Siegfried Nijssen; Luc De Raedt (SDM, 2009)

Cluster-grouping: from subgroup discovery to clustering. - Albrecht Zimmermann; Luc De Raedt (Mach. Learn., 2009)

Deriving distance metrics from generality relations. - Luc De Raedt; Jan Ramon (Pattern Recognit. Lett., 2009)

A Theory of Inductive Query Answering. - Luc De Raedt; Manfred Jaeger; Sau Dan Lee; Heikki Mannila (Inductive Databases and Constraint-Based Data Mining, 2010)

Probabilistic Inductive Querying Using ProbLog. - Luc De Raedt; Angelika Kimmig; Bernd Gutmann; Kristian Kersting; Vítor Santos Costa; Hannu Toivonen (Inductive Databases and Constraint-Based Data Mining, 2010)

DTProbLog: A Decision-Theoretic Probabilistic Prolog. - Guy Van den Broeck; Ingo Thon; Martijn van Otterlo; Luc De Raedt (AAAI, 2010)

Constraint Programming for Data Mining and Machine Learning. - Luc De Raedt; Tias Guns; Siegfried Nijssen (AAAI, 2010)

ProbLog Technology for Inference in a Probabilistic First Order Logic. - Maurice Bruynooghe; Theofrastos Mantadelis; Angelika Kimmig; Bernd Gutmann; Joost Vennekens; Gerda Janssens; Luc De Raedt (ECAI, 2010)

Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses. - Laura Antanas; Martijn van Otterlo; José Oramas M.; Tinne Tuytelaars; Luc De Raedt (ILP, 2010)

Extending ProbLog with Continuous Distributions. - Bernd Gutmann; Manfred Jaeger; Luc De Raedt (ILP, 2010)

Probabilistic Rule Learning. - Luc De Raedt; Ingo Thon (ILP, 2010)

Fast learning of relational kernels. - Niels Landwehr; Andrea Passerini; Luc De Raedt; Paolo Frasconi (Mach. Learn., 2010)

Mining Predictive k-CNF Expressions. - Anton Dries; Luc De Raedt; Siegfried Nijssen (IEEE Trans. Knowl. Data Eng., 2010)

Inductive Logic Programming. - Luc De Raedt (Encyclopedia of Machine Learning, 2010)

Logic of Generality. - Luc De Raedt (Encyclopedia of Machine Learning, 2010)

Multi-Relational Data Mining. - Luc De Raedt (Encyclopedia of Machine Learning, 2010)

Statistical Relational Learning. - Luc De Raedt; Kristian Kersting (Encyclopedia of Machine Learning, 2010)

About Knowledge and Inference in Logical and Relational Learning. - Luc De Raedt (Advances in Machine Learning II, 2010)

An Algebraic Prolog for Reasoning about Possible Worlds. - Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (AAAI, 2011)

Declarative Heuristic Search for Pattern Set Mining. - Tias Guns; Siegfried Nijssen; Albrecht Zimmermann; Luc De Raedt (ICDM Workshops, 2011)

Lifted Probabilistic Inference by First-Order Knowledge Compilation. - Guy Van den Broeck; Nima Taghipour; Wannes Meert; Jesse Davis; Luc De Raedt (IJCAI, 2011)

Relational Learning for Spatial Relation Extraction from Natural Language. - Parisa Kordjamshidi; Paolo Frasconi; Martijn van Otterlo; Marie-Francine Moens; Luc De Raedt (ILP, 2011)

Statistical Relational Learning of Object Affordances for Robotic Manipulation. - Bogdan Moldovan; Martijn van Otterlo; Luc De Raedt; Plinio Moreno; José Santos-Victor (ILP (Late Breaking Papers), 2011)

Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection. - Mathias Verbeke; Paolo Frasconi; Vincent Van Asch; Roser Morante; Walter Daelemans; Luc De Raedt (ILP, 2011)

Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract). - Luc De Raedt; Siegfried Nijssen (ISMIS, 2011)

Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. - Tias Guns; Siegfried Nijssen; Luc De Raedt (PAKDD (2), 2011)

Learning the Parameters of Probabilistic Logic Programs from Interpretations. - Bernd Gutmann; Ingo Thon; Luc De Raedt (ECML/PKDD (1), 2011)

Inference in Probabilistic Logic Programs using Weighted CNF's. - Daan Fierens; Guy Van den Broeck; Ingo Thon; Bernd Gutmann; Luc De Raedt (UAI, 2011)

Itemset mining: A constraint programming perspective. - Tias Guns; Siegfried Nijssen; Luc De Raedt (Artif. Intell., 2011)

Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201). - Luc De Raedt; Siegfried Nijssen; Barry O'Sullivan; Pascal Van Hentenryck (Dagstuhl Reports, 2011)

Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. - Hendrik Blockeel; Karsten M. Borgwardt; Luc De Raedt; Pedro M. Domingos; Kristian Kersting; Xifeng Yan (Mach. Learn., 2011)

Effective feature construction by maximum common subgraph sampling. - Leander Schietgat; Fabrizio Costa; Jan Ramon; Luc De Raedt (Mach. Learn., 2011)

Stochastic relational processes: Efficient inference and applications. - Ingo Thon; Niels Landwehr; Luc De Raedt (Mach. Learn., 2011)

The magic of logical inference in probabilistic programming. - Bernd Gutmann; Ingo Thon; Angelika Kimmig; Maurice Bruynooghe; Luc De Raedt (Theory Pract. Log. Program., 2011)

On the implementation of the probabilistic logic programming language ProbLog. - Angelika Kimmig; Bart Demoen; Luc De Raedt; Vítor Santos Costa; Ricardo Rocha 001 (Theory Pract. Log. Program., 2011)

A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories. - Mathias Verbeke; Vincent Van Asch; Roser Morante; Paolo Frasconi; Walter Daelemans; Luc De Raedt (EMNLP-CoNLL, 2012)

Mining Local Staircase Patterns in Noisy Data. - Thanh Le Van; Ana Carolina Fierro; Tias Guns; Matthijs van Leeuwen; Siegfried Nijssen; Luc De Raedt; Kathleen Marchal (ICDM Workshops, 2012)

A Relational Distance-based Framework for Hierarchical Image Understanding. - Laura Antanas; Martijn van Otterlo; José Oramas M.; Tinne Tuytelaars; Luc De Raedt (ICPRAM (2), 2012)

Learning relational affordance models for robots in multi-object manipulation tasks. - Bogdan Moldovan; Plinio Moreno; Martijn van Otterlo; José Santos-Victor; Luc De Raedt (ICRA, 2012)

Declarative Modeling for Machine Learning and Data Mining. - Luc De Raedt (ECML/PKDD (1), 2012)

A Relational Kernel-Based Framework for Hierarchical Image Understanding. - Laura Antanas; Paolo Frasconi; Fabrizio Costa; Tinne Tuytelaars; Luc De Raedt (SSPR/SPR, 2012)

A Particle Filter for Probabilistic Dynamic Relational Domains. - Davide Nitti 001; Tinne De Laet; McElory Hoffmann; Ingo Thon; Guy Van den Broeck; Luc De Raedt (StarAI@UAI, 2012)

ILP turns 20 - Biography and future challenges. - Stephen H. Muggleton; Luc De Raedt; David Poole 001; Ivan Bratko; Peter A. Flach; Katsumi Inoue; Ashwin Srinivasan 001 (Mach. Learn., 2012)

BiQL: A Query Language for Analyzing Information Networks. - Anton Dries; Siegfried Nijssen; Luc De Raedt (Bisociative Knowledge Discovery, 2012)

Patterns and Logic for Reasoning with Networks. - Angelika Kimmig; Esther Galbrun; Hannu Toivonen; Luc De Raedt (Bisociative Knowledge Discovery, 2012)

10 Years of Probabilistic Querying - What Next? - Martin Theobald; Luc De Raedt; Maximilian Dylla; Angelika Kimmig; Iris Miliaraki (ADBIS, 2013)

MCMC Estimation of Conditional Probabilities in Probabilistic Programming Languages. - Bogdan Moldovan; Ingo Thon; Jesse Davis; Luc De Raedt (ECSQARU, 2013)

Statistical Relational Learning using ProbLog. - Luc De Raedt (KNOW@LOD, 2013)

Allocentric Pose Estimation. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (ICCV, 2013)

The MiningZinc Framework for Constraint-Based Itemset Mining. - Tias Guns; Anton Dries; Guido Tack; Siegfried Nijssen; Luc De Raedt (ICDM Workshops, 2013)

Active Preference Learning for Ranking Patterns. - Vladimir Dzyuba; Matthijs van Leeuwen; Siegfried Nijssen; Luc De Raedt (ICTAI, 2013)

MiningZinc: A Modeling Language for Constraint-Based Mining. - Tias Guns; Anton Dries; Guido Tack; Siegfried Nijssen; Luc De Raedt (IJCAI, 2013)

A particle filter for hybrid relational domains. - Davide Nitti 001; Tinne De Laet; Luc De Raedt (IROS, 2013)

A relational kernel-based approach to scene classification. - Laura Antanas; McElory Hoffmann; Paolo Frasconi; Tinne Tuytelaars; Luc De Raedt (WACV, 2013)

Mining closed patterns in relational, graph and network data. - Gemma C. Garriga; Roni Khardon; Luc De Raedt (Ann. Math. Artif. Intell., 2013)

Probabilistic Programming Concepts. - Luc De Raedt; Angelika Kimmig (CoRR, 2013)

k-Pattern Set Mining under Constraints. - Tias Guns; Siegfried Nijssen; Luc De Raedt (IEEE Trans. Knowl. Data Eng., 2013)

Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics. - Joris Renkens; Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (AAAI, 2014)

Efficient Probabilistic Inference for Dynamic Relational Models. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (StarAI@AAAI, 2014)

kLogNLP: Graph Kernel-based Relational Learning of Natural Language. - Mathias Verbeke; Paolo Frasconi; Kurt De Grave; Fabrizio Costa; Luc De Raedt (ACL (System Demonstrations), 2014)

Condition Monitoring with Incomplete Observations. - Jonas Vlasselaer; Wannes Meert; Rocco Langone; Luc De Raedt (ECAI, 2014)

Occluded object search by relational affordances. - Bogdan Moldovan; Luc De Raedt (ICRA, 2014)

Relational object tracking and learning. - Davide Nitti 001; Tinne De Laet; Luc De Raedt (ICRA, 2014)

PageRank, ProPPR, and Stochastic Logic Programs. - Dries Van Daele; Angelika Kimmig; Luc De Raedt (ILP, 2014)

Learning relational affordance models for two-arm robots. - Bogdan Moldovan; Luc De Raedt (IROS, 2014)

Distributional Clauses Particle Filter. - Davide Nitti 001; Tinne De Laet; Luc De Raedt (ECML/PKDD (3), 2014)

Ranked Tiling. - Thanh Le Van; Matthijs van Leeuwen; Siegfried Nijssen; Ana Carolina Fierro; Kathleen Marchal; Luc De Raedt (ECML/PKDD (2), 2014)

Relational Regularization and Feature Ranking. - Fabrizio Costa; Mathias Verbeke; Luc De Raedt (SDM, 2014)

Lazy and Eager Relational Learning Using Graph-Kernels. - Mathias Verbeke; Vincent Van Asch; Walter Daelemans; Luc De Raedt (SLSP, 2014)

Application of Dynamic Distributional Clauses for Multi-hypothesis Initialization in Model-based Object Tracking. - Davide Nitti 001; Georgios Chliveros; Maria Pateraki; Luc De Raedt; Emmanouil Hourdakis; Panos E. Trahanias (VISAPP (2), 2014)

Towards cautious collective inference for object verification. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (WACV, 2014)

kLog: A language for logical and relational learning with kernels. - Paolo Frasconi; Fabrizio Costa; Luc De Raedt; Kurt De Grave (Artif. Intell., 2014)

Introduction to the Special Issue on the ECAI 2012 Turing and Anniversary Track. - Maria Fox 001; Luc De Raedt (AI Commun., 2014)

High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline. - Laura Antanas; Plinio Moreno; Marion Neumann; Rui Pimentel de Figueiredo; Kristian Kersting; José Santos-Victor; Luc De Raedt (CoRR, 2014)

Constraints, Optimization and Data (Dagstuhl Seminar 14411). - Luc De Raedt; Siegfried Nijssen; Barry O'Sullivan; Michèle Sebag (Dagstuhl Reports, 2014)

Interactive Learning of Pattern Rankings. - Vladimir Dzyuba; Matthijs van Leeuwen; Siegfried Nijssen; Luc De Raedt (Int. J. Artif. Intell. Tools, 2014)

There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding. - Laura Antanas; Martijn van Otterlo; José Oramas Mogrovejo; Tinne Tuytelaars; Luc De Raedt (Neurocomputing, 2014)

Languages for Learning and Mining. - Luc De Raedt (AAAI, 2015)

Neural-Symbolic Learning and Reasoning: Contributions and Challenges. - 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 (AAAI Spring Symposia, 2015)

Constraint-Based Querying for Bayesian Network Exploration. - Behrouz Babaki; Tias Guns; Siegfried Nijssen; Luc De Raedt (IDA, 2015)

kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract). - Paolo Frasconi; Fabrizio Costa; Luc De Raedt; Kurt De Grave (IJCAI, 2015)

Graph Invariant Kernels. - Francesco Orsini; Paolo Frasconi; Luc De Raedt (IJCAI, 2015)

Inducing Probabilistic Relational Rules from Probabilistic Examples. - Luc De Raedt; Anton Dries; Ingo Thon; Guy Van den Broeck; Mathias Verbeke (IJCAI, 2015)

Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (IJCAI, 2015)

Relational Kernel-Based Grasping with Numerical Features. - Laura Antanas; Plinio Moreno; Luc De Raedt (ILP, 2015)

kProbLog: An Algebraic Prolog for Kernel Programming. - Francesco Orsini; Paolo Frasconi; Luc De Raedt (ILP, 2015)

An Exercise in Declarative Modeling for Relational Query Mining. - Sergey Paramonov 001; Matthijs van Leeuwen; Marc Denecker; Luc De Raedt (ILP, 2015)

Rank Matrix Factorisation. - Thanh Le Van; Matthijs van Leeuwen; Siegfried Nijssen; Luc De Raedt (PAKDD (1), 2015)

ProbLog2: Probabilistic Logic Programming. - Anton Dries; Angelika Kimmig; Wannes Meert; Joris Renkens; Guy Van den Broeck; Jonas Vlasselaer; Luc De Raedt (ECML/PKDD (3), 2015)

Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. - Davide Nitti 001; Vaishak Belle; Luc De Raedt (ECML/PKDD (2), 2015)

Lazy Explanation-Based Approximation for Probabilistic Logic Programming. - Joris Renkens; Angelika Kimmig; Luc De Raedt (CoRR, 2015)

Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling. - Albrecht Zimmermann; Björn Bringmann; Luc De Raedt (CoRR, 2015)

Probabilistic (logic) programming concepts. - Luc De Raedt; Angelika Kimmig (Mach. Learn., 2015)

PheNetic: network-based interpretation of molecular profiling data. - Dries De Maeyer; Bram Weytjens; Joris Renkens; Luc De Raedt; Kathleen Marchal (Nucleic Acids Res., 2015)

Introduction to the special issue on probability, logic and learning. - James Cussens; Luc De Raedt; Angelika Kimmig; Taisuke Sato (Theory Pract. Log. Program., 2015)

Inference and learning in probabilistic logic programs using weighted Boolean formulas. - Daan Fierens; Guy Van den Broeck; Joris Renkens; Dimitar Sht. Shterionov; Bernd Gutmann; Ingo Thon; Gerda Janssens; Luc De Raedt (Theory Pract. Log. Program., 2015)

Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. - Jonas Vlasselaer; Angelika Kimmig; Anton Dries; Wannes Meert; Luc De Raedt (AAAI Workshop: Beyond NP, 2016)

Learning the Structure of Dynamic Hybrid Relational Models. - Davide Nitti 001; Irma Ravkic; Jesse Davis; Luc De Raedt (ECAI, 2016)

Qualitative Spatial Reasoning for Soccer Pass Prediction. - Vincent Vercruyssen; Luc De Raedt; Jesse Davis (MLSA@PKDD/ECML, 2016)

Exploiting local and repeated structure in Dynamic Bayesian Networks. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (Artif. Intell., 2016)

Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. - 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 (Bioinform., 2016)

Semiring Programming: A Framework for Search, Inference and Learning. - Vaishak Belle; Luc De Raedt (CoRR, 2016)

TP-Compilation for inference in probabilistic logic programs. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (Int. J. Approx. Reason., 2016)

Introduction to Special issue on "The Sixth International Symposium on Information and Communication Technology -SoICT 2015. - Luc De Raedt; Yves Deville; Marc Bui; Truong Thi Dieu Linh (Informatica (Slovenia), 2016)

Probabilistic logic programming for hybrid relational domains. - Davide Nitti 001; Tinne De Laet; Luc De Raedt (Mach. Learn., 2016)

The Inductive Constraint Programming Loop. - Christian Bessiere; Luc De Raedt; Tias Guns; Lars Kotthoff; Mirco Nanni; Siegfried Nijssen; Barry O'Sullivan; Anastasia Paparrizou; Dino Pedreschi; Helmut Simonis (Data Mining and Constraint Programming, 2016)

Modeling in MiningZinc. - Anton Dries; Tias Guns; Siegfried Nijssen; Behrouz Babaki; Thanh Le Van; Benjamin Négrevergne; Sergey Paramonov 001; Luc De Raedt (Data Mining and Constraint Programming, 2016)

Learning Constraint Satisfaction Problems: An ILP Perspective. - Luc De Raedt; Anton Dries; Tias Guns; Christian Bessiere (Data Mining and Constraint Programming, 2016)

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation - Luc De Raedt; Kristian Kersting; Sriraam Natarajan; David Poole 001 (Statistical Relational Artificial Intelligence, 2016)

TaCLe: Learning Constraints in Tabular Data. - Sergey Paramonov 001; Samuel Kolb; Tias Guns; Luc De Raedt (CIKM, 2017)

Stochastic Constraint Programming with And-Or Branch-and-Bound. - Behrouz Babaki; Tias Guns; Luc De Raedt (IJCAI, 2017)

Solving Probability Problems in Natural Language. - Anton Dries; Angelika Kimmig; Jesse Davis; Vaishak Belle; Luc De Raedt (IJCAI, 2017)

Relational Affordance Learning for Task-Dependent Robot Grasping. - Laura Antanas; Anton Dries; Plinio Moreno; Luc De Raedt (ILP, 2017)

Towards Automated Relational Data Wrangling. - Gust Verbruggen; Luc De Raedt (AutoML@PKDD/ECML, 2017)

MiningZinc: A declarative framework for constraint-based mining. - Tias Guns; Anton Dries; Siegfried Nijssen; Guido Tack; Luc De Raedt (Artif. Intell., 2017)

Context-based object viewpoint estimation: A 2D relational approach. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (Comput. Vis. Image Underst., 2017)

Flexible constrained sampling with guarantees for pattern mining. - Vladimir Dzyuba; Matthijs van Leeuwen; Luc De Raedt (Data Min. Knowl. Discov., 2017)

The Inductive Constraint Programming Loop. - Christian Bessiere; Luc De Raedt; Tias Guns; Lars Kotthoff; Mirco Nanni; Siegfried Nijssen; Barry O'Sullivan; Anastasia Paparrizou; Dino Pedreschi; Helmut Simonis (IEEE Intell. Syst., 2017)

Editors' Introduction to the Special Issue on "Information and Communication Technology". - Luc De Raedt; Marc Bui; Yves Deville; T. Dieu Linh Truong (Informatica (Slovenia), 2017)

Algebraic model counting. - Angelika Kimmig; Guy Van den Broeck; Luc De Raedt (J. Appl. Log., 2017)

Learning constraints in spreadsheets and tabular data. - Samuel Kolb; Sergey Paramonov 001; Tias Guns; Luc De Raedt (Mach. Learn., 2017)

Planning in hybrid relational MDPs. - Davide Nitti 001; Vaishak Belle; Tinne De Laet; Luc De Raedt (Mach. Learn., 2017)

kProbLog: an algebraic Prolog for machine learning. - Francesco Orsini; Paolo Frasconi; Luc De Raedt (Mach. Learn., 2017)

Relational data factorization. - Sergey Paramonov 001; Matthijs van Leeuwen; Luc De Raedt (Mach. Learn., 2017)

Semiring Rank Matrix Factorization. - Thanh Le Van; Siegfried Nijssen; Matthijs van Leeuwen; Luc De Raedt (IEEE Trans. Knowl. Data Eng., 2017)

Inductive Logic Programming. - Luc De Raedt (Encyclopedia of Machine Learning and Data Mining, 2017)

Logic of Generality. - Luc De Raedt (Encyclopedia of Machine Learning and Data Mining, 2017)

Multi-relational Data Mining. - Luc De Raedt (Encyclopedia of Machine Learning and Data Mining, 2017)

Statistical Relational Learning. - Luc De Raedt; Kristian Kersting (Encyclopedia of Machine Learning and Data Mining, 2017)

Learning Constraints From Examples. - Luc De Raedt; Andrea Passerini; Stefano Teso (AAAI, 2018)

Sketched Answer Set Programming. - Sergey Paramonov 001; Christian Bessiere; Anton Dries; Luc De Raedt (ICTAI, 2018)

Elements of an Automatic Data Scientist. - Luc De Raedt; Hendrik Blockeel; Samuel Kolb; Stefano Teso; Gust Verbruggen (IDA, 2018)

Automatically Wrangling Spreadsheets into Machine Learning Data Formats. - Gust Verbruggen; Luc De Raedt (IDA, 2018)

Learning SMT(LRA) Constraints using SMT Solvers. - Samuel Kolb; Stefano Teso; Andrea Passerini; Luc De Raedt (IJCAI, 2018)

DeepProbLog: Neural Probabilistic Logic Programming. - Robin Manhaeve; Sebastijan Dumancic; Angelika Kimmig; Thomas Demeester; Luc De Raedt (NeurIPS, 2018)

Relational affordances for multiple-object manipulation. - Bogdan Moldovan; Plinio Moreno; Davide Nitti 001; José Santos-Victor; Luc De Raedt (Auton. Robots, 2018)

Automating Personnel Rostering by Learning Constraints Using Tensors. - Mohit Kumar 003; Stefano Teso; Luc De Raedt (CoRR, 2018)

Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming. - Pedro Zuidberg Dos Martires; Anton Dries; Luc De Raedt (CoRR, 2018)

Automating Data Science (Dagstuhl Seminar 18401). - Tijl De Bie; Luc De Raedt; Holger H. Hoos; Padhraic Smyth (Dagstuhl Reports, 2018)

Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge Compilation. - Pedro Zuidberg Dos Martires; Anton Dries; Luc De Raedt (AAAI, 2019)

Scalable Rule Learning in Probabilistic Knowledge Bases. - Arcchit Jain; Tal Friedman; Ondrej Kuzelka; Guy Van den Broeck; Luc De Raedt (AKBC, 2019)

Automating Personnel Rostering by Learning Constraints Using Tensors. - Mohit Kumar 003; Stefano Teso; Patrick De Causmaecker; Luc De Raedt (ICTAI, 2019)

The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration. - Samuel Kolb; Paolo Morettin; Pedro Zuidberg Dos Martires; Francesco Sommavilla; Andrea Passerini; Roberto Sebastiani; Luc De Raedt (IJCAI, 2019)

Acquiring Integer Programs from Data. - Mohit Kumar 003; Stefano Teso; Luc De Raedt (IJCAI, 2019)

Neuro-Symbolic = Neural + Logical + Probabilistic. - Luc De Raedt; Robin Manhaeve; Sebastijan Dumancic; Thomas Demeester; Angelika Kimmig (NeSy@IJCAI, 2019)

SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract). - Yann Dauxais; Clément Gautrais; Anton Dries; Arcchit Jain; Samuel Kolb; Mohit Kumar 003; Stefano Teso; Elia Van Wolputte; Gust Verbruggen; Luc De Raedt (PKDD/ECML Workshops (1), 2019)

How to Exploit Structure while Solving Weighted Model Integration Problems. - Samuel Kolb; Pedro Zuidberg Dos Martires; Luc De Raedt (UAI, 2019)

Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. - Laura Antanas; Plinio Moreno; Marion Neumann; Rui Pimentel de Figueiredo; Kristian Kersting; José Santos-Victor; Luc De Raedt (Auton. Robots, 2019)

Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations. - Ozan Arkan Can; Pedro Zuidberg Dos Martires; Andreas Persson; Julian Gaal; Amy Loutfi; Luc De Raedt; Deniz Yuret; Alessandro Saffiotti (CoRR, 2019)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). - Luc De Raedt; Richard Evans 001; Stephen H. Muggleton; Ute Schmid (Dagstuhl Reports, 2019)

Algebraic Circuits for Decision Theoretic Inference and Learning. - Vincent Derkinderen; Luc De Raedt (ECAI, 2020)

Discovering Textual Structures: Generative Grammar Induction using Template Trees. - Thomas Winters; Luc De Raedt (ICCC, 2020)

ProbAnch: a Modular Probabilistic Anchoring Framework. - Andreas Persson; Pedro Zuidberg Dos Martires; Luc De Raedt; Amy Loutfi (IJCAI, 2020)

From Statistical Relational to Neuro-Symbolic Artificial Intelligence. - Luc De Raedt; Sebastijan Dumancic; Robin Manhaeve; Giuseppe Marra (IJCAI, 2020)

VisualSynth: Democratizing Data Science in Spreadsheets. - Clément Gautrais; Yann Dauxais; Samuel Kolb; Arcchit Jain; Mohit Kumar 003; Stefano Teso; Elia Van Wolputte; Gust Verbruggen; Luc De Raedt (ECML/PKDD (5), 2020)

Ordering Variables for Weighted Model Integration. - Vincent Derkinderen; Evert Heylen; Pedro Zuidberg Dos Martires; Samuel Kolb; Luc De Raedt (UAI, 2020)

Human-Machine Collaboration for Democratizing Data Science. - Clément Gautrais; Yann Dauxais; Stefano Teso; Samuel Kolb; Gust Verbruggen; Luc De Raedt (CoRR, 2020)

Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring. - Pedro Zuidberg Dos Martires; Nitesh Kumar; Andreas Persson; Amy Loutfi; Luc De Raedt (Frontiers Robotics AI, 2020)

Semiring programming: A semantic framework for generalized sum product problems. - Vaishak Belle; Luc De Raedt (Int. J. Approx. Reason., 2020)

Predictive spreadsheet autocompletion with constraints. - Samuel Kolb; Stefano Teso; Anton Dries; Luc De Raedt (Mach. Learn., 2020)

Semantic Relational Object Tracking. - Andreas Persson; Pedro Zuidberg Dos Martires; Luc De Raedt; Amy Loutfi (IEEE Trans. Cogn. Dev. Syst., 2020)

Democratizing Constraint Satisfaction Problems through Machine Learning. - Mohit Kumar 003; Samuel Kolb; Clément Gautrais; Luc De Raedt (AAAI, 2021)

Mapping probability word problems to executable representations. - Simon Suster; Pieter Fivez; Pietro Totis; Angelika Kimmig; Jesse Davis; Luc De Raedt; Walter Daelemans (EMNLP (1), 2021)

Shape Inference and Grammar Induction for Example-Based Procedural Generation. - Gillis Hermans; Thomas Winters; Luc De Raedt (ICCC, 2021)

SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints. - Dirko Coetsee; Steve Kroon; McElory Hoffmann; Luc De Raedt (IDA, 2021)

Muppets: Multipurpose Table Segmentation. - Gust Verbruggen; Lidia Contreras Ochando; Cèsar Ferri; José Hernández-Orallo; Luc De Raedt (IDA, 2021)

avatar - Automated Feature Wrangling for Machine Learning. - Gust Verbruggen; Elia Van Wolputte; Sebastijan Dumancic; Luc De Raedt (IDA, 2021)

Learning CNF Theories Using MDL and Predicate Invention. - Arcchit Jain; Clément Gautrais; Angelika Kimmig; Luc De Raedt (IJCAI, 2021)

Approximate Inference for Neural Probabilistic Logic Programming. - Robin Manhaeve; Giuseppe Marra; Luc De Raedt (KR, 2021)

Neural probabilistic logic programming in DeepProbLog. - Robin Manhaeve; Sebastijan Dumancic; Angelika Kimmig; Thomas Demeester; Luc De Raedt (Artif. Intell., 2021)

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer. - Maaike Van Roy; Pieter Robberechts; Wen-Chi Yang; Luc De Raedt; Jesse Davis (CoRR, 2021)

Automating Data Science: Prospects and Challenges. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (CoRR, 2021)

Learning Mixed-Integer Linear Programs from Contextual Examples. - Mohit Kumar 003; Samuel Kolb; Luc De Raedt; Stefano Teso (CoRR, 2021)

From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey. - Giuseppe Marra; Sebastijan Dumancic; Robin Manhaeve; Luc De Raedt (CoRR, 2021)

A Table-Based Representation for Probabilistic Logic: Preliminary Results. - Simon Vandevelde; Victor Verreet; Luc De Raedt; Joost Vennekens (CoRR, 2021)

SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation. - Pietro Totis; Angelika Kimmig; Luc De Raedt (CoRR, 2021)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 21192). - Andrew Cropper; Luc De Raedt; Richard Evans 001; Ute Schmid (Dagstuhl Reports, 2021)

Neuro-Symbolic AI = Neural + Logical + Probabilistic AI. - Robin Manhaeve; Giuseppe Marra; Thomas Demeester; Sebastijan Dumancic; Angelika Kimmig; Luc De Raedt (Neuro-Symbolic Artificial Intelligence, 2021)

Human-Machine Collaboration for Democratizing Data Science. - Clément Gautrais; Yann Dauxais; Stefano Teso; Samuel Kolb; Gust Verbruggen; Luc De Raedt (Human-Like Machine Intelligence, 2022)

Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. - Victor Verreet; Vincent Derkinderen; Pedro Zuidberg Dos Martires; Luc De Raedt (AAAI, 2022)

DeepStochLog: Neural Stochastic Logic Programming. - Thomas Winters; Giuseppe Marra; Robin Manhaeve; Luc De Raedt (AAAI, 2022)

Parameter Learning in ProbLog with Annotated Disjunctions. - Wen-Chi Yang; Arcchit Jain; Luc De Raedt; Wannes Meert (IDA, 2022)

OMEN: network-based driver gene identification using mutual exclusivity. - Dries Van Daele; Bram Weytjens; Luc De Raedt; Kathleen Marchal (Bioinform., 2022)

Automating data science. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (Commun. ACM, 2022)

Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains. - Gavin Rens; Wen-Chi Yang; Jean-François Raskin; Luc De Raedt (CoRR, 2022)

Lifted model checking for relational MDPs. - Wen-Chi Yang; Jean-François Raskin; Luc De Raedt (Mach. Learn., 2022)

Introduction to the Collection of Papers Celebrating the 20th Anniversary of TPLP. - Thomas Eiter; Michael J. Maher; Enrico Pontelli; Luc De Raedt; Miroslaw Truszczynski (Theory Pract. Log. Program., 2022)

Learning Distributional Programs for Relational Autocompletion. - Nitesh Kumar; Ondrej Kuzelka; Luc De Raedt (Theory Pract. Log. Program., 2022)

Safe Reinforcement Learning via Probabilistic Logic Shields. - Wen-Chi Yang; Giuseppe Marra; Gavin Rens; Luc De Raedt (IJCAI, 2023)

Soft-Unification in Deep Probabilistic Logic. - Jaron Maene; Luc De Raedt (NeurIPS, 2023)

Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. - Rishi Hazra; Luc De Raedt (ECML/PKDD (4), 2023)

Neural probabilistic logic programming in discrete-continuous domains. - Lennert De Smet; Pedro Zuidberg Dos Martires; Robin Manhaeve; Giuseppe Marra; Angelika Kimmig; Luc De Raedt (UAI, 2023)

Learning MAX-SAT from contextual examples for combinatorial optimisation. - Mohit Kumar 003; Samuel Kolb; Stefano Teso; Luc De Raedt (Artif. Intell., 2023)

smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation. - Pietro Totis; Angelika Kimmig; Luc De Raedt (CoRR, 2023)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 23442). - Luc De Raedt; Ute Schmid; Johannes Langer (Dagstuhl Reports, 2023)

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. - Nitesh Kumar; Ondrej Kuzelka; Luc De Raedt (J. Artif. Intell. Res., 2023)

A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer. - Maaike Van Roy; Pieter Robberechts; Wen-Chi Yang; Luc De Raedt; Jesse Davis (J. Artif. Intell. Res., 2023)

Lifted Reasoning for Combinatorial Counting. - Pietro Totis; Jesse Davis; Luc De Raedt; Angelika Kimmig (J. Artif. Intell. Res., 2023)

The Collection of Papers Celebrating the 20th Anniversary of TPLP, Part II. - Thomas Eiter; Michael J. Maher; Enrico Pontelli; Luc De Raedt; Miroslaw Truszczynski (Theory Pract. Log. Program., 2023)

smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation. - Pietro Totis; Luc De Raedt; Angelika Kimmig (Theory Pract. Log. Program., 2023)

SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge. - Rishi Hazra; Pedro Zuidberg Dos Martires; Luc De Raedt (AAAI, 2024)

Inference and Learning in Dynamic Decision Networks Using Knowledge Compilation. - Gabriele Venturato; Vincent Derkinderen; Pedro Zuidberg Dos Martires; Luc De Raedt (AAAI, 2024)

CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments. - Savitha Sam Abraham; Marjan Alirezaie; Luc De Raedt (LREC/COLING, 2024)

Approximate Compression of CNF Concepts. - Sieben Bocklandt; Vincent Derkinderen; Angelika Kimmig; Luc De Raedt (DS (2), 2024)

EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic. - Victor Verreet; Lennert De Smet; Luc De Raedt; Emanuele Sansone (ECAI, 2024)

On the Hardness of Probabilistic Neurosymbolic Learning. - Jaron Maene; Vincent Derkinderen; Luc De Raedt (ICML, 2024)

Subgraph Mining for Graph Neural Networks. - Adem Kikaj; Giuseppe Marra; Luc De Raedt (IDA (1), 2024)

Automated Reasoning in Systems Biology: A Necessity for Precision Medicine. - Pedro Zuidberg Dos Martires; Vincent Derkinderen; Luc De Raedt; Marcus Krantz (KR, 2024)

Valid Text-to-SQL Generation with Unification-Based DeepStochLog. - Ying Jiao; Luc De Raedt; Giuseppe Marra (NeSy (1), 2024)

Declarative probabilistic logic programming in discrete-continuous domains. - Pedro Zuidberg Dos Martires; Luc De Raedt; Angelika Kimmig (Artif. Intell., 2024)

Can Large Language Models Reason? A Characterization via 3-SAT. - Rishi Hazra; Gabriele Venturato; Pedro Zuidberg Dos Martires; Luc De Raedt (CoRR, 2024)

NeSyA: Neurosymbolic Automata. - Nikolaos Manginas; George Paliouras; Luc De Raedt (CoRR, 2024)

Semirings for probabilistic and neuro-symbolic logic programming. - Vincent Derkinderen; Robin Manhaeve; Pedro Zuidberg Dos Martires; Luc De Raedt (Int. J. Approx. Reason., 2024)

Modeling PU learning using probabilistic logic programming. - Victor Verreet; Luc De Raedt; Jessa Bekker (Mach. Learn., 2024)

From statistical relational to neurosymbolic artificial intelligence: A survey. - Giuseppe Marra; Sebastijan Dumancic; Robin Manhaeve; Luc De Raedt (Artif. Intell., 2024)

Neurosymbolic Reinforcement Learning: Playing MiniHack with Probabilistic Logic Shields. - David Debot; Gabriele Venturato; Giuseppe Marra; Luc De Raedt (AAAI, 2025)

The Gradient of Algebraic Model Counting. - Jaron Maene; Luc De Raedt (AAAI, 2025)

Relational Neurosymbolic Markov Models. - Lennert De Smet; Gabriele Venturato; Luc De Raedt; Giuseppe Marra (AAAI, 2025)

Neurosymbolic Decision Trees. - Matthias Möller; Arvid Norlander; Pedro Zuidberg Dos Martires; Luc De Raedt (CoRR, 2025)

Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition. - Rishi Hazra; Gabriele Venturato; Pedro Zuidberg Dos Martires; Luc De Raedt (CoRR, 2025)

Defining neurosymbolic AI. - Lennert De Smet; Luc De Raedt (CoRR, 2025)

Correction to: Modeling PU learning using probabilistic logic programming. - Victor Verreet; Luc De Raedt; Jessa Bekker (Mach. Learn., 2025)

The DeepLog Neurosymbolic Machine. - Vincent Derkinderen; Robin Manhaeve; Rik Adriaensen; Lucas Van Praet; Lennert De Smet; Giuseppe Marra; Luc De Raedt (CoRR, 2025)