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

A Framework for Defining Distances Between First-Order Logic Objects. - Jan Ramon; Maurice Bruynooghe (ILP, 1998)

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)

Learning a Go Heuristic with TILDE. - Jan Ramon; Tom Francis; Hendrik Blockeel (Computers and Games, 2000)

Executing Query Packs in ILP. - Hendrik Blockeel; Luc Dehaspe; Bart Demoen; Gerda Janssens; Jan Ramon; Henk Vandecasteele (ILP, 2000)

Using Belief Networks to Neutralize Known Dependencies in Conceptual Clustering. - Jan Ramon; Luc Dehaspe (ILP Work-in-progress reports, 2000)

Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner. - Kurt Driessens; Jan Ramon; Hendrik Blockeel (ECML, 2001)

A polynomial time computable metric between point sets. - Jan Ramon; Maurice Bruynooghe (Acta Informatica, 2001)

Compact Representation of Knowledge Bases in ILP. - Jan Struyf; Jan Ramon; Hendrik Blockeel (ILP, 2002)

Clustering and instance based learning in first order logic. - Jan Ramon (AI Commun., 2002)

Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs. - Hendrik Blockeel; Luc Dehaspe; Bart Demoen; Gerda Janssens; Jan Ramon; Henk Vandecasteele (J. Artif. Intell. Res., 2002)

Relational Instance Based Regression for Relational Reinforcement Learning. - Kurt Driessens; Jan Ramon (ICML, 2003)

Searching for Compound Goals Using Relevancy Zones in the Game of Go. - Jan Ramon; Tom Croonenborghs (Computers and Games, 2004)

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

Compact Representation of Knowledge Bases in Inductive Logic Programming. - Jan Struyf; Jan Ramon; Maurice Bruynooghe; Sofie Verbaeten; Hendrik Blockeel (Mach. Learn., 2004)

Logical Bayesian Networks and Their Relation to Other Probabilistic Logical Models. - Daan Fierens; Hendrik Blockeel; Maurice Bruynooghe; Jan Ramon (BNAIC, 2005)

A Comparison of Approaches for Learning Probability Trees. - Daan Fierens; Jan Ramon; Hendrik Blockeel; Maurice Bruynooghe (ECML, 2005)

Multi-agent Relational Reinforcement Learning. - Tom Croonenborghs; Karl Tuyls; Jan Ramon; Maurice Bruynooghe (LAMAS, 2005)

ReMauve: A Relational Model Tree Learner. - Celine Vens; Jan Ramon; Hendrik Blockeel (ILP, 2006)

Refining Aggregate Conditions in Relational Learning. - Celine Vens; Jan Ramon; Hendrik Blockeel (PKDD, 2006)

Graph kernels and Gaussian processes for relational reinforcement learning. - Kurt Driessens; Jan Ramon; Thomas Gärtner 001 (Mach. Learn., 2006)

On Policy Learning in Restricted Policy Spaces. - Robby Goetschalckx; Jan Ramon (AAAI, 2007)

Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling. - Jan Ramon; Kurt Driessens; Tom Croonenborghs (ECML, 2007)

Online Learning and Exploiting Relational Models in Reinforcement Learning. - Tom Croonenborghs; Jan Ramon; Hendrik Blockeel; Maurice Bruynooghe (IJCAI, 2007)

Learning Directed Probabilistic Logical Models Using Ordering-Search. - Daan Fierens; Jan Ramon; Maurice Bruynooghe; Hendrik Blockeel (ILP, 2007)

General Graph Refinement with Polynomial Delay. - Jan Ramon; Siegfried Nijssen (MLG, 2007)

A Polynomial-time Metric for Outerplanar Graphs. - Leander Schietgat; Jan Ramon; Maurice Bruynooghe (MLG, 2007)

Mining data from intensive care patients. - Jan Ramon; Daan Fierens; Fabian Güiza Grandas; Geert Meyfroidt; Hendrik Blockeel; Maurice Bruynooghe; Greta Van den Berghe (Adv. Eng. Informatics, 2007)

Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker. - Marc J. V. Ponsen; Jan Ramon; Tom Croonenborghs; Kurt Driessens; Karl Tuyls (AAAI, 2008)

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

An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules. - Leander Schietgat; Jan Ramon; Maurice Bruynooghe; Hendrik Blockeel (Discovery Science, 2008)

Using Decision Trees as the Answer Networks in Temporal Difference-Networks. - Laura-Andreea Antanas; Kurt Driessens; Jan Ramon; Tom Croonenborghs (ECAI, 2008)

Anti-monotonic Overlap-Graph Support Measures. - Toon Calders; Jan Ramon; Dries Van Dyck (ICDM, 2008)

Min, Max and PTIME Anti-Monotonic Overlap Graph Measures. - Toon Calders; Jan Ramon; Dries Van Dyck (MLG@ICML, 2008)

Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth. - Tamás Horváth 001; Jan Ramon (ECML/PKDD (1), 2008)

Learning directed probabilistic logical models: ordering-search versus structure-search. - Daan Fierens; Jan Ramon; Maurice Bruynooghe; Hendrik Blockeel (Ann. Math. Artif. Intell., 2008)

Generalized ordering-search for learning directed probabilistic logical models. - Jan Ramon; Tom Croonenborghs; Daan Fierens; Hendrik Blockeel; Maurice Bruynooghe (Mach. Learn., 2008)

Monte-Carlo Tree Search in Poker Using Expected Reward Distributions. - Guy Van den Broeck; Kurt Driessens; Jan Ramon (ACML, 2009)

Learning with whom to communicate using relational reinforcement learning. - Marc J. V. Ponsen; Tom Croonenborghs; Karl Tuyls; Jan Ramon; Kurt Driessens (AAMAS (2), 2009)

Handling missing values and censored data in PCA of pharmacological matrices. - Jan Ramon; Fabrizio Costa (KDD Workshop on Statistical and Relational Learning in Bioinformatics, 2009)

Dynamic Data Analysis and Data Mining for Prediction of Clinical Stability. - Kristien Van Loon; Fabian Güiza Grandas; Geert Meyfroidt; Jean-Marie Aerts; Jan Ramon; Hendrik Blockeel; Maurice Bruynooghe; Greta Van den Berghe; Daniel Berckmans (MIE, 2009)

Context-free Graph Grammars as a Language-bias Mechanism for Graph Pattern Mining. - Christophe Costa Florêncio; Jan Ramon; Jonny Daenen; Jan Van den Bussche; Dries Van Dyck (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)

An evolutionary game-theoretic analysis of poker strategies. - Marc J. V. Ponsen; Karl Tuyls; Michael Kaisers; Jan Ramon (Entertain. Comput., 2009)

Polynomial-Delay Enumeration of Monotonic Graph Classes. - Jan Ramon; Siegfried Nijssen (J. Mach. Learn. Res., 2009)

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

StReBio'09: statistical relational learning and mining in bioinformatics. - Jan Ramon; Fabrizio Costa; Christophe Costa Florêncio; Joost N. Kok (SIGKDD Explor., 2009)

Inductive Queries for a Drug Designing Robot Scientist. - Ross D. King; Amanda C. Schierz; Amanda Clare; Jem J. Rowland; Andrew Sparkes; Siegfried Nijssen; Jan Ramon (Inductive Databases and Constraint-Based Data Mining, 2010)

Frequent subgraph mining in outerplanar graphs. - Tamás Horváth 001; Jan Ramon; Stefan Wrobel (Data Min. Knowl. Discov., 2010)

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis. - Kristien Van Loon; Fabian Güiza Grandas; Geert Meyfroidt; Jean-Marie Aerts; Jan Ramon; Hendrik Blockeel; Maurice Bruynooghe; Greta Van den Berghe; Daniel Berckmans (J. Medical Syst., 2010)

A comparison of pruning criteria for probability trees. - Daan Fierens; Jan Ramon; Hendrik Blockeel; Maurice Bruynooghe (Mach. Learn., 2010)

Efficient frequent connected subgraph mining in graphs of bounded tree-width. - Tamás Horváth 001; Jan Ramon (Theor. Comput. Sci., 2010)

Learning with Whom to Communicate Using Relational Reinforcement Learning. - Marc J. V. Ponsen; Tom Croonenborghs; Karl Tuyls; Jan Ramon; Kurt Driessens; H. Jaap van den Herik; Eric O. Postma (Interactive Collaborative Information Systems, 2010)

All normalized anti-monotonic overlap graph measures are bounded. - Toon Calders; Jan Ramon; Dries Van Dyck (Data Min. Knowl. Discov., 2011)

Preface. - Jan Ramon; Fabrizio Costa; Christophe Costa Florêncio; Joost N. Kok (Fundam. Informaticae, 2011)

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model. - Geert Meyfroidt; Fabian Güiza Grandas; Dominiek Cottem; Wilfried De Becker; Kristien Van Loon; Jean-Marie Aerts; Daniel Berckmans; Jan Ramon; Maurice Bruynooghe; Greta Van den Berghe (BMC Medical Informatics Decis. Mak., 2011)

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

Modeling Machine Learning and Data Mining Problems with FO(·). - Hendrik Blockeel; Bart Bogaerts 001; Maurice Bruynooghe; Broes De Cat; Stef De Pooter; Marc Denecker; Anthony Labarre; Jan Ramon; Sicco Verwer (ICLP (Technical Communications), 2012)

Hybrid Logical Bayesian Networks. - Irma Ravkic; Jan Ramon; Jesse Davis (ILP (Late Breaking Papers), 2012)

An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining. - Yuyi Wang 001; Jan Ramon (ECML/PKDD (1), 2012)

Open Problem: Learning Dynamic Network Models from a Static Snapshot. - Jan Ramon; Constantin Comendant (COLT, 2012)

Guided Monte Carlo Tree Search for Planning in Learned Environments. - Jelle Van Eyck; Jan Ramon; Fabian Güiza Grandas; Geert Meyfroidt; Maurice Bruynooghe; Greta Van den Berghe (ACML, 2013)

Efficient Frequent Connected Induced Subgraph Mining in Graphs of Bounded Tree-Width. - Tamás Horváth 001; Keisuke Otaki; Jan Ramon (ECML/PKDD (1), 2013)

Detecting Bicliques in GF[q]. - Jan Ramon; Pauli Miettinen; Jilles Vreeken (ECML/PKDD (1), 2013)

A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics. - Leander Schietgat; Jan Ramon; Maurice Bruynooghe (Ann. Math. Artif. Intell., 2013)

PIUS: peptide identification by unbiased search. - Eduardo P. Costa; Gerben Menschaert; Walter Luyten; Kurt De Grave; Jan Ramon (Bioinform., 2013)

Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3. - Maurice Bruynooghe; Hendrik Blockeel; Bart Bogaerts 001; Broes De Cat; Stef De Pooter; Joachim Jansen; Anthony Labarre; Jan Ramon; Marc Denecker; Sicco Verwer (CoRR, 2013)

Learning from networked examples in a k-partite graph. - Yuyi Wang 001; Jan Ramon; Zheng-Chu Guo (CoRR, 2013)

Nearly exact mining of frequent trees in large networks. - Ashraf M. Kibriya; Jan Ramon (Data Min. Knowl. Discov., 2013)

An efficiently computable subgraph pattern support measure: counting independent observations. - Yuyi Wang 001; Jan Ramon; Thomas Fannes (Data Min. Knowl. Discov., 2013)

Graph and Network Pattern Mining. - Jan Ramon; Constantin Comendant; Mostafa Haghir Chehreghani; Yuyi Wang 001 (Mining User Generated Content, 2014)

Towards a Framework for Learning from Networked Data. - Jan Ramon (ICCS, 2014)

U-statistics on network-structured data with kernels of degree larger than one. - Yuyi Wang 001; Christos Pelekis; Jan Ramon (SSDM@ECML/PKDD, 2014)

Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel. - Leander Schietgat; Thomas Fannes; Jan Ramon (Discovery Science, 2015)

Mine 'Em All: A Note on Mining All Graphs. - Ondrej Kuzelka; Jan Ramon (ILP, 2015)

A Note on Restricted Forms of LGG. - Ondrej Kuzelka; Jan Ramon (ILP (Late Breaking Papers), 2015)

Naive Infinite Enumeration of Context-free Languages in Incremental Polynomial Time. - Christophe Costa Florêncio; Jonny Daenen; Jan Ramon; Jan Van den Bussche; Dries Van Dyck (J. Univers. Comput. Sci., 2015)

Learning relational dependency networks in hybrid domains. - Irma Ravkic; Jan Ramon; Jesse Davis (Mach. Learn., 2015)

Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3. - Maurice Bruynooghe; Hendrik Blockeel; Bart Bogaerts 001; Broes De Cat; Stef De Pooter; Joachim Jansen; Anthony Labarre; Jan Ramon; Marc Denecker; Sicco Verwer (Theory Pract. Log. Program., 2015)

Bounds for Learning from Evolutionary-Related Data in the Realizable Case. - Ondrej Kuzelka; Yuyi Wang 001; Jan Ramon (IJCAI, 2016)

Guest editors introduction: special issue on inductive logic programming. - Jesse Davis; Jan Ramon (Mach. Learn., 2016)

Learning from Networked Examples. - Yuyi Wang 001; Zheng-Chu Guo; Jan Ramon (ALT, 2017)

Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries. - Pierre Dellenbach; Aurélien Bellet; Jan Ramon (CoRR, 2018)

Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model. - Irma Ravkic; Martin Znidarsic; Jan Ramon; Jesse Davis (Data Min. Knowl. Discov., 2018)

A machine learning based framework to identify and classify long terminal repeat retrotransposons. - Leander Schietgat; Celine Vens; Ricardo Cerri; Carlos N. Fischer; Eduardo P. Costa; Jan Ramon; Claudia M. A. Carareto; Hendrik Blockeel (PLoS Comput. Biol., 2018)

Dependency Weighted Aggregation on Factorized Databases. - Florent Capelli; Nicolas Crosetti; Joachim Niehren; Jan Ramon (CoRR, 2019)

Interpretable Privacy with Optimizable Utility. - Jan Ramon; Moitree Basu (PKDD/ECML Workshops, 2020)

Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. - César Sabater; Aurélien Bellet; Jan Ramon (CoRR, 2020)

Linear Programs with Conjunctive Queries. - Florent Capelli; Nicolas Crosetti; Joachim Niehren; Jan Ramon (ICDT, 2022)

An accurate, scalable and verifiable protocol for federated differentially private averaging. - César Sabater; Aurélien Bellet; Jan Ramon (Mach. Learn., 2022)

Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies. - Alberto Pedrouzo-Ulloa; Jan Ramon; Fernando Pérez-González; Siyanna Lilova; Patrick Duflot; Zakaria Chihani; Nicola Gentili; Paola Ulivi; Mohammad Ashadul Hoque; Twaha Mukammel; Zeev Pritzker; Augustin Lemesle; et al. (CSR, 2023)

DP-SGD with weight clipping. - Antoine Barczewski; Jan Ramon (CoRR, 2023)

Limits of multi-relational graphs. - Juan Alvarado; Yuyi Wang 001; Jan Ramon (Mach. Learn., 2023)

Private Sampling with Identifiable Cheaters. - César Sabater; Florian Hahn 001; Andreas Peter 001; Jan Ramon (Proc. Priv. Enhancing Technol., 2023)

Honest Fraction Differential Privacy. - Imane Taibi; Jan Ramon (IH&MMSec, 2024)

SoK: Verifiable Cross-Silo FL. - Aleksei Korneev; Jan Ramon (CoRR, 2024)

Linear Programs with Conjunctive Database Queries. - Florent Capelli; Nicolas Crosetti; Joachim Niehren; Jan Ramon (Log. Methods Comput. Sci., 2024)

A Survey on Verifiable Cross-Silo Federated Learning. - Aleksei Korneev; Jan Ramon (Trans. Mach. Learn. Res., 2025)

Differentially Private Empirical Cumulative Distribution Functions. - Antoine Barczewski; Amal Mawass; Jan Ramon (CoRR, 2025)

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation. - César Sabater; Sonia Ben Mokhtar; Jan Ramon (CoRR, 2025)

How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations. - Marc Damie; Florian Hahn 001; Andreas Peter 001; Jan Ramon (CoRR, 2025)