Machine Learning and Artificial Intelligence

Figure : Sports sciences through data sciences (Louvain-la-Neuve Running Heatmap)

ICTEAM research activities in this field are conducted by seven primary investigators and about fourty researchers. There are two main domains of activity : Machine Learning and Constraint Programming.

Principal Investigators :

Pierre-Antoine Absil, Jean-Charles Delvenne, Yves Deville, Pierre Dupont, John Lee, Siegfried Nijssen, Marco Saerens, Pierre Schaus, Michel Verleysen, Vincent Wertz

Research Labs :

Machine Learning Group, Constraint Group

Research areas :

The research carried out by the UCL Machine Learning Group (MLG) covers both fundamental and applied aspects of machine learning.

Machine learning aims at mining large collection of data and at building models to predict future data. This multidisciplinary field has links to statistics, signal processing, information theory and optimization. It also covers a wide range of applications such as biomedical data analysis, image and video analysis, time series prediction, graph mining, natural language processing, ...

The group specifically addresses the following topics:

  • High-dimensional, functional and non-linear data analysis
  • Feature and model selection
  • Data visualization and manifold learning
  • Bayesian learning
  • Biomedical signal processing and analysis, including ECG, EEG, and respiratory signal analysis, and medical image filtering
  • High-throughput biological data analysis, including microarray data analysis and next-generation sequencing
  • Temporal series prediction, including electrical workload prediction, financial time-series forecasting, networking measurement prediction
  • Automata and Grammar induction with application to software system modeling
  • Structured data analysis, graph mining and collaborative filtering

The UCL Machine Learning Group is organizing, on a yearly basis since 1993, the European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Constraint Programming (CP) is a powerful paradigm for modelling and solving complex combinatorial (optimization) problems. It integrates techniques from artificial intelligence, computer science, operational research and optimization. CP separates the modelling of the problem from the search for solutions. It offers high level modelling languages based on constraints. CP proposes two complementary search mechanisms. Standard CP is based on systematic tree search coupled with pruning techniques to remove infeasable solutions. Constraint-Based Local Search (CBLS) allows heuristic search based on the exploration of neighborhoods. The Constraint Group is mainly interested in consistency techniques, integration of CP and CBLS, graph matching, routing problems, applications in networking, ...

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all publications.


Journal Articles


1. Soemers, Dennis J.N.J.; Kowalski, Jakub; Piette, Eric; Morenville, Achille; Crist, Walter. GameTable Working Group 1 meeting report on search, planning, learning, and explainability. In: ICGA Journal, Vol. 46, no.1, p. 28-35 (2024). doi:10.3233/icg-240251. http://hdl.handle.net/2078.1/291979

2. Piette, Eric; Crist, Walter; Soemers, Dennis J.N.J.; Rougetet, Lisa; Courts, Summer; Penn, Tim; Morenville, Achille. GameTable COST action: kickoff report. In: ICGA Journal, Vol. 45, no. 4, p. 1-17 (2024). doi:10.3233/icg-240245. http://hdl.handle.net/2078.1/286762

3. Dennis J.N.J. Soemers; Mella Vegard; Piette, Eric; Matthew Stephenson; Cameron Browne; Olivier Teytaud. Towards a General Transfer Approach for Policy-Value Networks. In: Transactions on Machine Learning Research, (2023). http://hdl.handle.net/2078.1/281298

4. Vermeylen, Charlotte; Olikier, Guillaume; Absil, Pierre-Antoine; Van Barel, Marc. Rank Estimation for Third-Order Tensor Completion in the Tensor-Train Format. In: Proceedings of the 31st European Signal Processing Conference (EUSIPCO 2023, , p. 965-969 (2023). doi:10.48550/arXiv.2309.15170. http://hdl.handle.net/2078.1/281058

5. van den Elzen, Stef; Andrienko, Gennady; Andrienko, Natalia; Fisher, Brian D; Martins, Rafael M; Peltonen, Jaakko; Telea, Alexandru C; Verleysen, Michel. The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications. In: IEEE Computer Graphics and Applications, Vol. 43, no.2, p. 78-88 (2023). http://hdl.handle.net/2078.1/280914

6. Piette, Eric; Soemers, Dennis J.N.J.; Stephenson, Matthew; Browne, Cameron. The 2022 Ludii AI competition. In: ICGA Journal, Vol. 45, no.1, p. 16-27 (2023). doi:10.3233/icg-230230. http://hdl.handle.net/2078.1/279535

7. Soemers, Dennis J.N.J.; Samothrakis, Spyridon; Piette, Eric; Stephenson, Matthew. Extracting tactics learned from self-play in general games. In: Information Sciences, Vol. 624, no. 1, p. 277-298 (2023). doi:10.1016/j.ins.2022.12.080. http://hdl.handle.net/2078/276412

8. Soemers, Dennis J.N.J.; Piette, Eric; Stephenson, Matthew; Browne, Cameron. Spatial state-action features for general games. In: Artificial Intelligence, Vol. 321, no. 103937, p. 32 (2023). doi:10.1016/j.artint.2023.103937. http://hdl.handle.net/2078/276408

9. Cartis, Coralia; Massart, Estelle; Otemissov, Adilet. Global optimization using random embeddings. In: Mathematical Programming, Vol. 200, no.2, p. 781-829 (2022). doi:10.1007/s10107-022-01871-y. http://hdl.handle.net/2078.1/289358

10. Cartis, Coralia; Massart, Estelle; Otemissov, Adilet. Bound-constrained global optimization of functions with low effective dimensionality using multiple random embeddings. In: Mathematical Programming, Vol. 198, no.1, p. 997-1058 (2022). doi:10.1007/s10107-022-01812-9. http://hdl.handle.net/2078.1/289238


Conference Papers


1. Morenville, Achille; Piette, Eric. Belief Stochastic Game: A Model for Imperfect-Information Games with Known Positions. 2024 xxx. http://hdl.handle.net/2078.1/293483

2. Dennis J.N.J. Soemers; Vegard Mella; Piette, Eric; Matthew Stephenson; Cameron Browne; Olivier Teytaud. Encore Abstract: Towards a General Transfer Approach for Policy-Value Networks. 2024 xxx. http://hdl.handle.net/2078.1/292649

3. Dorina Moullou; Walter Crist; Timothy Penn; Piette, Eric. Gametable Network: Unveiling the Past, Embracing the Future Through AI-Driven Archaeological Research. 2024 xxx. http://hdl.handle.net/2078.1/292646

4. Todd, Graham; Padula, Alexander; Stephenson, Matthew; Piette, Eric; Soemers, Dennis; Togelius, Julian. GAVEL: Generating Games Via Evolution and Language Models. 2024 xxx. http://hdl.handle.net/2078.1/291987

5. Soemers, Dennis J.N.J.; Piette, Eric; Stephenson, Matthew; Browne, Cameron. The Ludii Game Description Language is Universal. In: 2024 IEEE Conference on Games (CoG), 2024, 979-8-3503-5067-8 xxx. doi:10.1109/cog60054.2024.10645550. http://hdl.handle.net/2078.1/291980

6. Sadre, Wei; Huet-Dastarac, Margerie; Deffet, Sylvain; Sterpin, Edmond; Barragan Montero, Ana Maria; Jodogne, Sébastien; Lee, John Aldo. PARROT - A Versatile Platform for AI-Driven Image Segmentation and Dose Prediction. In: Proc. of the 5th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024). 2024 xxx. http://hdl.handle.net/2078.1/291340

7. Langlois, Quentin; Jodogne, Sébastien. Embeddings for Motor Imagery Classification. In: Proc. of IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP 2024). p. 1-6. IEEE, 2024 xxx. doi:10.1109/MLSP58920.2024.10734730. http://hdl.handle.net/2078.1/291339

8. Chatzopoulos, Edouard; Jodogne, Sébastien. Assessing the Impact of Deep Learning Backbones for Mass Detection in Breast Imaging. In: Lecture Notes in Computer Science. Vol. 14976, p. 33-47 (2024). Springer Nature Switzerland: Cham, Switzerland, 2024 xxx. doi:10.1007/978-3-031-67285-9_3. http://hdl.handle.net/2078.1/289661

9. Chatzopoulos, Edouard; Jodogne, Sébastien. Integrated and Interoperable Platform for Detecting Masses on Mammograms. In: Studies in Health Technology and Informatics. Vol. 316, p. 1103-1107 (2024). IOS Press, 2024 xxx. doi:10.3233/SHTI240603. http://hdl.handle.net/2078.1/289660

10. Morenville, Achille; Piette, Eric. Vers une Approche Polyvalente pour les Jeux à Information Imparfaite sans Connaissance de Domaine. 2024 xxx. http://hdl.handle.net/2078.1/287704