| 2025 | ICDM | TabFairGDT: A Fast Fair Tabular Data Generator Using Autoregressive Decision Trees. | Emmanouil Panagiotou, Benot Ronval, Arjun Roy, Ludwig Bothmann, Bernd Bischl, Siegfried Nijssen, Eirini Ntoutsi |
| 2025 | IJCAI | Learning from Logical Constraints with Lower- and Upper-Bound Arithmetic Circuits. | Lucile Dierckx, Alexandre Dubray, Siegfried Nijssen |
| 2025 | IDA | Detection of Large Language Model Contamination with Tabular Data. | Benot Ronval, Pierre Dupont, Siegfried Nijssen |
| 2024 | CP | Anytime Weighted Model Counting with Approximation Guarantees for Probabilistic Inference. | Alexandre Dubray, Pierre Schaus, Siegfried Nijssen |
| 2024 | CPAIOR | An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems. | Bastin Vjar, Gal Aglin, Ali Irfan Mahmutogullari, Siegfried Nijssen, Pierre Schaus, Tias Guns |
| 2024 | DIS | MID: A New Strategy for Learning Optimal Decision Trees on Continuous Data. | Antonio Dal Maso, Harold Silvre Kiossou, Siegfried Nijssen |
| 2024 | IDA | Efficient Lookahead Decision Trees. | Harold Silvre Kiossou, Pierre Schaus, Siegfried Nijssen, Gal Aglin |
| 2024 | IDA | Interpretable Quantile Regression by Optimal Decision Trees. | Valentin Lemaire, Gal Aglin, Siegfried Nijssen |
| 2024 | NeSy | Parameter Learning Using Approximate Model Counting. | Lucile Dierckx, Alexandre Dubray, Siegfried Nijssen |
| 2023 | CP | Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses. | Alexandre Dubray, Pierre Schaus, Siegfried Nijssen |
| 2023 | CP | Partitioning a Map into Homogeneous Contiguous Regions: A Branch-And-Bound Approach Using Decision Diagrams (Short Paper). | Nicolas Golenvaux, Xavier Gillard, Siegfried Nijssen, Pierre Schaus |
| 2023 | NeSy | RL-Net: Interpretable Rule Learning with Neural Networks. | Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen |
| 2023 | PAKDD | RL-Net: Interpretable Rule Learning with Neural Networks. | Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen |
| 2022 | DIS | Optimal Decoding of Hidden Markov Models with Consistency Constraints. | Alexandre Dubray, Guillaume Derval, Siegfried Nijssen, Pierre Schaus |
| 2022 | IDA | Detection and Multi-label Classification of Bats. | Lucile Dierckx, Mlanie Beauvois, Siegfried Nijssen |
| 2021 | ICTAI | Assessing Optimal Forests of Decision Trees. | Gal Aglin, Siegfried Nijssen, Pierre Schaus |
| 2020 | AAAI | Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. | Gal Aglin, Siegfried Nijssen, Pierre Schaus |
| 2020 | AAAI | Constraint Programming for an Efficient and Flexible Block Modeling Solver. | Alex Luca Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus |
| 2020 | DIS | Mining Constrained Regions of Interest: An Optimization Approach. | Alexandre Dubray, Guillaume Derval, Siegfried Nijssen, Pierre Schaus |
| 2020 | IJCAI | PyDL8.5: a Library for Learning Optimal Decision Trees. | Gal Aglin, Siegfried Nijssen, Pierre Schaus |
| 2020 | IJCAI | Learning Optimal Decision Trees using Constraint Programming (Extended Abstract). | Hlne Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus |
| 2019 | CP | Modeling Pattern Set Mining Using Boolean Circuits. | John O. R. Aoga, Siegfried Nijssen, Pierre Schaus |
| 2019 | CP | Generic Constraint-Based Block Modeling Using Constraint Programming. | Alex Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus |
| 2019 | DIS | Mining Patterns in Source Code Using Tree Mining Algorithms. | Hoang-Son Pham, Siegfried Nijssen, Kim Mens, Dario Di Nucci, Tim Molderez, Coen De Roover, Johan Fabry, Vadim Zaytsev |
| 2019 | IJCAI | Stochastic Constraint Propagation for Mining Probabilistic Networks. | Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen |
| 2018 | DIS | Finding Probabilistic Rule Lists using the Minimum Description Length Principle. | John O. R. Aoga, Tias Guns, Siegfried Nijssen, Pierre Schaus |
| 2018 | IDA | ConvoMap: Using Convolution to Order Boolean Data. | Thomas Bollen, Guillaume Leurquin, Siegfried Nijssen |
| 2017 | CP | Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving. | Anna L. D. Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck, Siegfried Nijssen |
| 2017 | IDA | Biclustering Multivariate Time Series. | Ricardo Cachucho, Siegfried Nijssen, Arno J. Knobbe |
| 2015 | ICDM | Finding Subspace Clusters Using Ranked Neighborhoods. | Emin Aksehirli, Siegfried Nijssen, Matthijs van Leeuwen, Bart Goethals |
| 2015 | IDA | Constraint-Based Querying for Bayesian Network Exploration. | Behrouz Babaki, Tias Guns, Siegfried Nijssen, Luc De Raedt |
| 2015 | PAKDD | Rank Matrix Factorisation. | Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt |
| 2014 | CPAIOR | Constrained Clustering Using Column Generation. | Behrouz Babaki, Tias Guns, Siegfried Nijssen |
| 2013 | AAAI | Formalizing Hierarchical Clustering as Integer Linear Programming. | Sean Gilpin, Siegfried Nijssen, Ian N. Davidson |
| 2013 | CIKM | Mining characteristic multi-scale motifs in sensor-based time series. | Ugo Vespier, Siegfried Nijssen, Arno J. Knobbe |
| 2013 | ICDM | The MiningZinc Framework for Constraint-Based Itemset Mining. | Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt |
| 2013 | ICDM | Dominance Programming for Itemset Mining. | Benjamin Ngrevergne, Anton Dries, Tias Guns, Siegfried Nijssen |
| 2013 | IJCAI | MiningZinc: A Modeling Language for Constraint-Based Mining. | Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt |
| 2013 | ICTAI | Active Preference Learning for Ranking Patterns. | Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt |
| 2012 | ICDM | Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data. | Michael Mampaey, Siegfried Nijssen, Ad Feelders, Arno J. Knobbe |
| 2012 | ICDM | 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 |
| 2012 | SDM | Mining Patterns in Networks using Homomorphism. | Anton Dries, Siegfried Nijssen |
| 2011 | ICDM | Declarative Heuristic Search for Pattern Set Mining. | Tias Guns, Siegfried Nijssen, Albrecht Zimmermann, Luc De Raedt |
| 2011 | ICDM | Constraint-Based Pattern Mining in Multi-relational Databases. | Siegfried Nijssen, Ada Jimnez, Tias Guns |
| 2011 | ILP | k-Optimal: A Novel Approximate Inference Algorithm for ProbLog. | Joris Renkens, Guy Van den Broeck, Siegfried Nijssen |
| 2011 | ISMIS | Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract). | Luc De Raedt, Siegfried Nijssen |
| 2011 | PAKDD | Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. | Tias Guns, Siegfried Nijssen, Luc De Raedt |
| 2010 | AAAI | Constraint Programming for Data Mining and Machine Learning. | Luc De Raedt, Tias Guns, Siegfried Nijssen |
| 2009 | CIKM | A query language for analyzing networks. | Anton Dries, Siegfried Nijssen, Luc De Raedt |
| 2009 | KDD | Correlated itemset mining in ROC space: a constraint programming approach. | Siegfried Nijssen, Tias Guns, Luc De Raedt |
| 2009 | SDM | Grammar Mining. | Siegfried Nijssen, Luc De Raedt |
| 2008 | ICML | Bayes optimal classification for decision trees. | Siegfried Nijssen |
| 2008 | KDD | Constraint programming for itemset mining. | Luc De Raedt, Tias Guns, Siegfried Nijssen |
| 2008 | PAKDD | What Is Frequent in a Single Graph?. | Bjrn Bringmann, Siegfried Nijssen |
| 2007 | KDD | Mining optimal decision trees from itemset lattices. | Siegfried Nijssen, lisa Fromont |
| 2004 | ECAI | Ideal Refinement of Datalog Clauses Using Primary Keys. | Siegfried Nijssen, Joost N. Kok |
| 2004 | KDD | A quickstart in frequent structure mining can make a difference. | Siegfried Nijssen, Joost N. Kok |
| 2004 | SMC | Frequent graph mining and its application to molecular databases. | Siegfried Nijssen, Joost N. Kok |
| 2001 | IJCAI | Faster Association Rules for Multiple Relations. | Siegfried Nijssen, Joost N. Kok |