| 2025 | CIKM | Content-Agnostic Moderation for Stance-Neutral Recommendations. | Nan Li, Bo Kang, Tijl De Bie |
| 2025 | EMNLP | Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration. | Nan Li, Bo Kang, Tijl De Bie |
| 2024 | ICLR | fairret: a Framework for Differentiable Fairness Regularization Terms. | Maarten Buyl, MaryBeth Defrance, Tijl De Bie |
| 2024 | RecSys | Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024). | Toine Bogers, David Graus, Mesut Kaya, Chris Johnson, Jens-Joris Decorte, Tijl De Bie |
| 2023 | RecSys | ReCon: Reducing Congestion in Job Recommendation using Optimal Transport. | Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie |
| 2022 | AISTATS | The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? | Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys |
| 2022 | ESANN | Embedding-based next song recommendation for playlists. | Raphal Romero, Tijl De Bie |
| 2022 | ICLR | Topologically Regularized Data Embeddings. | Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys |
| 2022 | KI | Mining Interesting Outlier Subgraphs in Attributed Graphs. | Ahmad Mel, Tijl De Bie |
| 2021 | DSAA | Conditional t-SNE: More informative t-SNE embeddings. | Bo Kang, Dario Garca-Garca, Jefrey Lijffijt, Ral Santos-Rodrguez, Tijl De Bie |
| 2020 | AIES | FACE: Feasible and Actionable Counterfactual Explanations. | Rafael Poyiadzi, Kacper Sokol, Ral Santos-Rodrguez, Tijl De Bie, Peter A. Flach |
| 2020 | CIKM | CSNE: Conditional Signed Network Embedding. | Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie |
| 2020 | DSAA | Block-Approximated Exponential Random Graphs. | Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie |
| 2020 | DSAA | Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress? | Alexandru Cristian Mara, Jefrey Lijffijt, Tijl De Bie |
| 2020 | DSAA | FONDUE: Framework for Node Disambiguation Using Network Embeddings. | Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie |
| 2020 | ICML | DeBayes: a Bayesian Method for Debiasing Network Embeddings. | Maarten Buyl, Tijl De Bie |
| 2020 | ICPR | Graph Approximations to Geodesics on Metric Graphs. | Robin Vandaele, Yvan Saeys, Tijl De Bie |
| 2020 | IDA | Gibbs Sampling Subjectively Interesting Tiles. | Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Cline Robardet, Tijl De Bie |
| 2020 | SDM | Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. | Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie |
| 2019 | CIKM | Discovering Interesting Cycles in Directed Graphs. | Florian Adriaens, igdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt |
| 2019 | ICLR | Conditional Network Embeddings. | Bo Kang, Jefrey Lijffijt, Tijl De Bie |
| 2019 | ICLR | EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. | Alexandru Mara, Jefrey Lijffijt, Tijl De Bie |
| 2019 | KDD | Contrastive Antichains in Hierarchies. | Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, Cline Robardet, Tijl De Bie |
| 2019 | SDM | EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. | Alexandru Mara, Jefrey Lijffijt, Tijl De Bie |
| 2018 | ICDE | Subjectively Interesting Subgroup Discovery on Real-Valued Targets. | Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamki, Emilia Oikarinen, Tijl De Bie |
| 2018 | ICDE | Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach. | Kai Puolamki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie |
| 2018 | KDD | Quantifying and Minimizing Risk of Conflict in Social Networks. | Xi Chen, Jefrey Lijffijt, Tijl De Bie |
| 2017 | IDA | Hierarchical Novelty Detection. | Paolo Simeone, Ral Santos-Rodrguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie |
| 2016 | ESANN | Informative data projections: a framework and two examples. | Tijl De Bie, Jefrey Lijffijt, Ral Santos-Rodrguez, Bo Kang |
| 2016 | ICASSP | Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction. | Matt McVicar, Ral Santos-Rodriguez, Tijl De Bie |
| 2016 | ICDM | Direct Mining of Subjectively Interesting Relational Patterns. | Tias Guns, Achille Aknin, Jefrey Lijffijt, Tijl De Bie |
| 2016 | KDD | Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. | Bo Kang, Jefrey Lijffijt, Ral Santos-Rodriguez, Tijl De Bie |
| 2015 | DSAA | P-N-RMiner: A generic framework for mining interesting structured relational patterns. | Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, Tijl De Bie |
| 2014 | DSAA | Mining approximate multi-relational patterns. | Eirini Spyropoulou, Tijl De Bie |
| 2013 | DIS | Mining Interesting Patterns in Multi-relational Data with N-ary Relationships. | Eirini Spyropoulou, Tijl De Bie, Mario Boley |
| 2013 | IDA | Subjective Interestingness in Exploratory Data Mining. | Tijl De Bie |
| 2012 | ICPRAM | An Empirical Comparison of Label Prediction Algorithms on Automatically Inferred Networks. | Omar Ali, Giovanni Zappella, Tijl De Bie, Nello Cristianini |
| 2012 | IDA | Formalizing Complex Prior Information to Quantify Subjective Interestingness of Frequent Pattern Sets. | Kleanthis-Nikolaos Kontonasios, Tijl De Bie |
| 2011 | ICDM | Maximum Entropy Modelling for Assessing Results on Real-Valued Data. | Kleanthis-Nikolaos Kontonasios, Jilles Vreeken, Tijl De Bie |
| 2011 | ICDM | Interesting Multi-relational Patterns. | Eirini Spyropoulou, Tijl De Bie |
| 2011 | KDD | An information theoretic framework for data mining. | Tijl De Bie |
| 2011 | KDD | Refining causality: who copied from whom? | Tristan Mark Snowsill, Nick Fyson, Tijl De Bie, Nello Cristianini |
| 2011 | SIGMOD | NOAM: news outlets analysis and monitoring system. | Ilias N. Flaounas, Omar Ali, Marco Turchi, Tristan Snowsill, Florent Nicart, Tijl De Bie, Nello Cristianini |
| 2010 | SDM | An Information-Theoretic Approach to Finding Informative Noisy Tiles in Binary Databases. | Kleanthis-Nikolaos Kontonasios, Tijl De Bie |
| 2009 | EAMT | Learning to translate: a statistical and computational analysis. | Marco Turchi, Tijl De Bie, Nello Cristianini |
| 2009 | ESANN | Machine Learning with Labeled and Unlabeled Data. | Tijl De Bie, Thiago Turchetti Maia, Antnio de Pdua Braga |
| 2008 | PSB | Integrating Microarray and Proteomics Data to Predict the Response of Cetuximab in Patients with Rectal Cancer. | Anneleen Daemen, Olivier Gevaert, Tijl De Bie, Annelies Debucquoy, Jean-Pascal Machiels, Bart De Moor, Karin Haustermans |
| 2007 | ESANN | Deploying SDP for machine learning. | Tijl De Bie |
| 2007 | ESANN | A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. | Sndor Szedmk, Tijl De Bie, David R. Hardoon |
| 2007 | IDA | Learning to Align: A Statistical Approach. | Elisa Ricci, Tijl De Bie, Nello Cristianini |
| 2007 | ISMB | Kernel-based data fusion for gene prioritization. | Tijl De Bie, Lon-Charles Tranchevent, Liesbeth M. M. van Oeffelen, Yves Moreau |
| 2005 | PSB | Discovering Transcriptional Modules from Motif, Chip-Chip and Microarray Data. | Tijl De Bie, Patrick Monsieurs, Kristof Engelen, Bart De Moor, Nello Cristianini, Kathleen Marchal |
| 2004 | SSPR | Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data. | Tijl De Bie, Nello Cristianini |
| 2004 | SSPR | Learning from General Label Constraints. | Tijl De Bie, Johan A. K. Suykens, Bart De Moor |
| 2003 | ALT | Efficiently Learning the Metric with Side-Information. | Tijl De Bie, Michinari Momma, Nello Cristianini |