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 ten 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, Estelle Massart, Siegfried Nijssen, Marco Saerens, Pierre Schaus, Michel Verleysen

Research Labs :

Machine Learning Group, Constraint Group

Research areas :

The research carried out by the UCLouvain 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. Dirksen, Sjoerd; Genzel, Martin; Stollenwerk, Alexander; Jacques, Laurent. The Separation Capacity of Random Neural Networks. In: Journal of Machine Learning Research, Vol. 23, no.209, p. 1--47 (2022). (Accepté/Sous presse).

2. Olikier, Guillaume; Absil, Pierre-Antoine. On the Continuity of the Tangent Cone to the Determinantal Variety. In: Set-Valued and Variational Analysis, Vol. 30, p. 769-788 (2022). doi:10.1007/s11228-022-00629-0.

3. Van Hoorebeeck, Loïc; Absil, Pierre-Antoine; Papavasiliou, Anthony. Solving non-convex economic dispatch with valve-point effects and losses with guaranteed accuracy. In: International Journal of Electrical Power & Energy Systems, Vol. 134, p. 107143 (2022). doi:10.1016/j.ijepes.2021.107143.

4. Berger, Guillaume O.; Absil, Pierre-Antoine; De Lathauwer, Lieven; Jungers, Raphaël M.; Van Barel, Marc. Equivalent polyadic decompositions of matrix multiplication tensors. In: Journal of Computational and Applied Mathematics, Vol. 406, p. 113941 (2022). doi:10.1016/

5. Nguyen, Thanh Son; Absil, Pierre-Antoine; Gao, Bin; Stykel, Tatjana. Symplectic eigenvalue problem via trace minimization and Riemannian optimization. In: SIAM Journal on Matrix Analysis and Applications, Vol. 42, no.4, p. 1732-1757 (2021). doi:10.1137/21m1390621.

6. Gao, Bin; Absil, Pierre-Antoine. A Riemannian rank-adaptive method for low-rank matrix completion. In: Computational Optimization and Applications, (2021). doi:10.1007/s10589-021-00328-w.

7. Wang, Lei; Gao, Bin; Liu, Xin. Multipliers Correction Methods for Optimization Problems over the Stiefel Manifold. In: CSIAM Transactions on Applied Mathematics, Vol. 2, no.3, p. 508-531 (2021). doi:10.4208/

8. Gao, Bin; Nguyen, Thanh Son; Absil, Pierre-Antoine; Stykel, Tatjana. Riemannian Optimization on the Symplectic Stiefel Manifold. In: SIAM Journal on Optimization, Vol. 31, no.2, p. 1546-1575 (2021). doi:10.1137/20m1348522.

9. Hamer, Victor; Dupont, Pierre. An Importance Weighted Feature Selection Stability Measure. In: Journal of Machine Learning Research, Vol. 22, no.116, p. 1-57 (2021).

10. Marrinan, Tim; Absil, Pierre-Antoine; Gillis, Nicolas. On a minimum enclosing ball of a collection of linear subspaces. In: Linear Algebra and its Applications, Vol. 625, p. 248-278 (2021). doi:10.1016/j.laa.2021.05.006.

Conference Papers

1. Jodogne, Sébastien. Client-Side Application of Deep Learning Models Through Teleradiology. In: Studies in Health Technology and Informatics. Vol. 302, no.1, p. 997-1001 (2023). Maria Hägglund et al. 2023 xxx. doi:10.3233/shti230325.

2. Gerniers, Alexander; Dupont, Pierre. MicroCellClust 2: a hybrid approach for multivariate rare cell mining in large-scale single-cell data. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, 978-1-6654-6819-0, p. 148-153 xxx. doi:10.1109/bibm55620.2022.9995176.

3. Jodogne, Sébastien. Importing and serving open-data medical images to support Artificial Intelligence research. In: Insights into Imaging. Vol. 13, no. S1, p. 6. SpringerOpen, 2021 xxx. doi:10.1186/s13244-022-01168-w.

4. Hamer, Victor; Dupont, Pierre. Robust Selection Stability Estimation in Correlated Spaces. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Vol. 1, no.1, p. 446-461 (2021). Springer, 2021 xxx.

5. Gao, Bin; Nguyen, Thanh Son; Absil, Pierre-Antoine; Stykel, Tatjana. Geometry of the Symplectic Stiefel Manifold Endowed with the Euclidean Metric. In: Lecture Notes in Computer Science : Geometric Science of Information, Springer International Publishing,2021, 2021, 978-3-030-80208-0, p. 789-796 xxx. doi:10.1007/978-3-030-80209-7_85.

6. Valy, Dona; Verleysen, Michel; Chhun, Sophia. Data Augmentation and Text Recognition on Khmer Historical Manuscripts. 2020 xxx.

7. Hamer, Victor; Dupont, Pierre. Joint optimization of predictive performance and selection stability. In: ESANN 2020 - Proceedings. Vol. 1, no.1, p. 381-386 (2020). 2020 xxx.

8. de Smet, Dimitri; Francaux, Marc; Baijot, Laurent; Verleysen, Michel. MAP Best Performances Prediction for Endurance Runners. 2019 xxx.

9. Hamer, Victor; Dupont, Pierre. Explicit Control of Feature Relevance and Selection Stability Through Pareto Optimality. In: CEUR Workshop Proceedings. Vol. 2444, no.1, p. 64-79 (2019). CEUR, 2019 xxx.

10. Nguyen, Thanh Son; Gousenbourger, Pierre-Yves; Massart, Estelle; Absil, Pierre-Antoine. Online balanced truncation for linear time-varying systems using continuously differentiable interpolation on Grassmann manifold. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, 2019, 9781728105215, p. 165-170 xxx. doi:10.1109/codit.2019.8820675.