Publications and communications of Raphaël Fonteneau

Ernst, D., & Fonteneau, R. (2023). Patrimoine Religieux et Transition Energétique. Paper presented at Journée d’étude « Le Patrimoine religieux liégeois dans tous ses états », Liège, Belgium.

Dachet, V., Benzerga, A., Fonteneau, R., & Ernst, D. (2023). Towards CO2 valorization in a multi remote renewable energy hub framework. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/301033.

Vangulick, D., Manuel de Villena Millan, M., Fonteneau, R., & Ernst, D. (2021). Residential Energy Communities: How to minimize the investment risk from an investor perspective. In Proceedings of the CIRED 2021 Conference.

Manuel de Villena Millan, M., Gautier, A., Ernst, D., Glavic, M., & Fonteneau, R. (March 2021). Modelling and Assessing the Impact of the DSO Remuneration Strategy on its Interaction with Electricity Users. International Journal of Electrical Power and Energy Systems, 126 (Part A), 106585. doi:10.1016/j.ijepes.2020.106585

Manuel de Villena Millan, M., Jacqmin, J., Fonteneau, R., Gautier, A., & Ernst, D. (2021). Network tariffs and the integration of prosumers: the Case of Wallonia. Energy Policy, 150. doi:10.1016/j.enpol.2020.112065

Radu, D.-C., Berger, M., Dubois, A., Fonteneau, R., Pandzic, H., Dvorkin, Y., Louveaux, Q., & Ernst, D. (2021). Assessing the Impact of Offshore Wind Siting Strategies on the Design of the European Power System. Applied Energy, 305. doi:10.1016/j.apenergy.2021.117700

Berger, M., Radu, D.-C., Ryszka, K., Ernst, D., Fonteneau, R., Detienne, G., & Deschuyteneer, T. (2020). The Role of Hydrogen in the Dutch Electricity System.

Berger, M., Radu, D.-C., Fonteneau, R., Deschuyteneer, T., Detienne, G., & Ernst, D. (March 2020). The Role of Power-to-Gas and Carbon Capture Technologies in Cross-Sector Decarbonisation Strategies. Electric Power Systems Research, 180. doi:10.1016/j.epsr.2019.106039

Berger, M., Radu, D.-C., Fonteneau, R., Henry, R., Glavic, M., Fettweis, X., Le Du, M., Panciatici, P., Balea, L., & Ernst, D. (05 February 2020). Critical Time Windows for Renewable Resource Complementarity Assessment. Energy, 198. doi:10.1016/j.energy.2020.117308

Aittahar, S., Fonteneau, R., & Ernst, D. (2020). Empirical Analysis of Policy Gradient Algorithms where Starting States are Sampled accordingly to Most Frequently Visited States. IFAC-PapersOnLine, 53 (2), 8097–8104. doi:10.1016/j.ifacol.2020.12.2279

Ernst, D., & Fonteneau, R. (2020). The Smart Grids lab at the University of Liège. Paper presented at Exchange program on renewables energies between the Walloon Parliament and US senators and deputies in the framework of the National Conference of States Legislature, Namur, Belgium.

Yu, J., Bakic, K., Kumar, A., Iliceto, A., Beleke Tabu, L., Ruaud, J. L., Fan, J., Cova, B., Li, H., Ernst, D., Fonteneau, R., Theku, M., Sanchis, G., Chamollet, M., Le Du, M., Zhang, Y., Chatzivasileiadis, S., Radu, D.-C., Berger, M., ... Ranjbar, M. (2019). Global electricity network - Feasibility study.

Radu, D.-C., Berger, M., Fonteneau, R., Hardy, S., Fettweis, X., Le Du, M., Panciatici, P., Balea, L., & Ernst, D. (15 May 2019). Complementarity Assessment of South Greenland Katabatic Flows and West Europe Wind Regimes. Energy, 175, 393-401. doi:10.1016/j.energy.2019.03.048

François-Lavet, V., Rabusseau, G., Pineau, J., Ernst, D., & Fonteneau, R. (May 2019). On overfitting and asymptotic bias in batch reinforcement learning with partial observability. Journal of Artificial Intelligence Research, 65, 1-30. doi:10.1613/jair.1.11478

Ernst, D., Berger, M., Fonteneau, R., & Radu, D.-C. (2019). Harnessing the Potential of Power-to-Gas Technologies. Insights from a preliminary analysis focused on Belgium.

Ernst, D., & Fonteneau, R. (2019). Un réseau électrique mondial et basé sur les renouvelables, ce n’est plus de la science-fiction. The Conversation.

Manuel de Villena Millan, M., Fonteneau, R., & Ernst, D. (2019). Tariff simulator. Paper presented at Transition énergétique : Consommateurs et réseaux, Liège, Belgium.

Manuel de Villena Millan, M., Fonteneau, R., Gautier, A., & Ernst, D. (March 2019). Evaluating the Evolution of Distribution Networks under Different Regulatory Frameworks with Multi-Agent Modelling. Energies, 12 (7), 1-15. doi:10.3390/en12071203

Berger, M., Radu, D.-C., Fonteneau, R., Ernst, D., Deschuyteneer, T., & Detienne, G. (2018). Centralised Planning of National Integrated Energy System with Power-to-Gas and Gas Storages. In Proceedings of the 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (Medpower2018). doi:10.1049/cp.2018.1912

Olivier, F., Fonteneau, R., Mathieu, S., & Ernst, D. (2018). Distributed Control of Photovoltaic Units in unbalanced LV Distribution Networks to Prevent Overvoltages. In Proc. of the 6th IEEE International Conference on Smart Energy Grid Engineering (SEGE 2018). doi:10.1109/SEGE.2018.8499515

Manuel de Villena Millan, M., Gautier, A., Fonteneau, R., & Ernst, D. (2018). A multi-agent system approach to model the interaction between distributed generation deployment and the grid. In Proc. of CIRED Workshop 2018.

Olivier, F., Fonteneau, R., & Ernst, D. (2018). Modelling of three-phase four-wire low-voltage cables taking into account the neutral connection to the earth. In Proc. of CIRED Workshop 2018.

Olivier, F., Sutera, A., Geurts, P., Fonteneau, R., & Ernst, D. (2018). Phase Identification of Smart Meters by Clustering Voltage Measurements. In Proceedings of the XX Power Systems Computation Conference (PSCC 2018). doi:10.23919/PSCC.2018.8442853

Manuel de Villena Millan, M., Fonteneau, R., Gautier, A., & Ernst, D. (2018). Exploring Regulation Policies in Distribution Networks through a Multi-Agent Simulator. In Proceedings of YRS2018.

Manuel de Villena Millan, M., Gautier, A., Fonteneau, R., & Ernst, D. (2017). Simulation-based Assessment of the Impact of Regulatory Frameworks on the Interactions between Electricity Users and Distribution System Operator. Eprint/Working paper retrieved from https://orbi.uliege.be/2268/215196.

Olivier, F., Marulli, D., Ernst, D., & Fonteneau, R. (2017). Foreseeing New Control Challenges in Electricity Prosumer Communities. In Proc. of the 10th Bulk Power Systems Dynamics and Control Symposium – IREP’2017.

Glavic, M., Fonteneau, R., & Ernst, D. (2017). Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives. In The 20th World Congress of the International Federation of Automatic Control, Toulouse 9-14 July 2017 (pp. 1-10). doi:10.1016/j.ifacol.2017.08.1217

Olivier, F., Ernst, D., & Fonteneau, R. (2017). Automatic phase identification of smart meter measurement data. In Proceedings of the 24th International Conference and Exhibition on Electricity Distribution, CIRED 2017. doi:10.1049/oap-cired.2017.1143

Fonteneau, R. (2017). A few reinforcement learning stories.

Castronovo, M., François-Lavet, V., Fonteneau, R., Ernst, D., & Couëtoux, A. (2017). Approximate Bayes Optimal Policy Search using Neural Networks. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017). doi:10.5220/0006191701420153

Dubois, A., Wehenkel, A., Fonteneau, R., Olivier, F., & Ernst, D. (2017). An App-based Algorithmic Approach for Harvesting Local and Renewable Energy Using Electric Vehicles. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017). doi:10.5220/0006250803220327

Fonteneau, R. (2017). Une histoire d’énergie : équations et transition.

Fonteneau, R. (2017). Artificial intelligence & energy transition: a few stories about energy prosumer communities. Paper presented at Journée scientifique de RISEGrid, Gif-sur-Yvette, France.

Fonteneau, R., & Ernst, D. (2017). On the Dynamics of the Deployment of Renewable Energy Production Capacities. In J. N. Furze, K. Swing, A. K. Gupta, R. H. McClatchey, ... D. M. Reynolds, Mathematical Advances Towards Sustainable Environmental Systems (pp. 43-60). Springer. doi:10.1007/978-3-319-43901-3_3

François-Lavet, V., Taralla, D., Ernst, D., & Fonteneau, R. (2016). Deep Reinforcement Learning Solutions for Energy Microgrids Management. In European Workshop on Reinforcement Learning (EWRL 2016).

Fonteneau, R. (2016). Les jeux vidéos : de formidables outils de test vers la résolution d’autres problèmes. Paper presented at Liège Créative - Séance de clôture, Liège, Belgium.

Castronovo, M., Ernst, D., Couëtoux, A., & Fonteneau, R. (2016). Benchmarking for Bayesian Reinforcement Learning. PLoS ONE. doi:10.1371/journal.pone.0157088

Gemine, Q., Cornélusse, B., Glavic, M., Fonteneau, R., & Ernst, D. (2016). A Gaussian mixture approach to model stochastic processes in power systems. In Proceedings of the 19th Power Systems Computation Conference (PSCC'16). doi:10.1109/PSCC.2016.7540921

François-Lavet, V., Gemine, Q., Ernst, D., & Fonteneau, R. (2016). Towards the Minimization of the Levelized Energy Costs of Microgrids using both Long-term and Short-term Storage Devices. In Smart Grid: Networking, Data Management, and Business Models (pp. 295-319). CRC Press.

Cornélusse, B., & Fonteneau, R. (02 February 2016). Artificial Intelligence and Energy. Paper presented at Liège Créative.

Taralla, D., Qiu, Z., Sutera, A., Fonteneau, R., & Ernst, D. (2016). Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning: A Study Intended for Large-Scale Video Games. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 2 (pp. 264-271). doi:10.5220/0005666202640271

Aittahar, S., François-Lavet, V., Lodeweyckx, S., Ernst, D., & Fonteneau, R. (2015). Imitative Learning for Online Planning in Microgrids. In W. L. Woon, A. Zeyar, ... M. Stuart (Eds.), Data Analytics for Renewable Energy Integration (pp. 1-15). Springer. doi:10.1007/978-3-319-27430-0_1

François-Lavet, V., Fonteneau, R., & Ernst, D. (2015). How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies. In NIPS 2015 Workshop on Deep Reinforcement Learning.

Fonteneau, R. (2015). On the Dynamics of the Deployment of Renewable Energy Production Capacities. Paper presented at Symposium 'R&D for the Energy Transition: the Role of Belgian Universities', Gent, Belgium.

Fonteneau, R. (2015). Une histoire d'énergie: équations et transition. Paper presented at Sustainable Energy Course, Liège, Belgium.

Safadi, F., Fonteneau, R., & Ernst, D. (13 March 2015). Artificial Intelligence in Video Games: Towards a Unified Framework. International Journal of Computer Games Technology, 2015, 30. doi:10.1155/2015/271296

Fonteneau, R. (2014). From Bad Models to Good Policies: an Intertwined Story about Energy and Reinforcement Learning. Paper presented at 2014 NIPS Workshop «From Bad Models to Good Policies Workshop (Sequential Decision Making under Uncertainty)», 
Montreal, December 12th, 2014
, Montreal, Canada.

François-Lavet, V., Fonteneau, R., & Ernst, D. (2014). Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device. IEEE Symposium Series on Computational Intelligence. doi:10.1109/ADPRL.2014.7010624

Fonteneau, R. (2014). Une Histoire d'Energie : Equations et Transition - Energy Stories, Equations and Transition. Paper presented at Pecha Kucha Night, Liège, Belgium.

Rivadeneira, P., Moog, C., Stan, G.-B., Brunet, C., Raffi, F., Ferré, V., Costanza, V., Mhawej, M.-J., Biafore, F., Ouattara, D., Ernst, D., Fonteneau, R., & Xia, X. (October 2014). Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Review. BioResearch Open Access, 3 (5), 233-241. doi:10.1089/biores.2014.0024

Moog, C., Rivadeneira, P., Stan, G.-B., Brunet, C., Raffi, F., Ferre, V., Costanza, V., Mhawej, M.-J., Ernst, D., Fonteneau, R., Biafore, F., Ouattara, D., & Xia, X. (September 2014). Mathematical modeling of HIV dynamics after antiretroviral therapy initiation: A clinical research study. AIDS Research and Human Retroviruses, 30 (9), 831-834. doi:10.1089/aid.2013.0286

Fonteneau, R. (2014). Energy Transition: How Can We Succeed? Paper presented at Scientizenship and Energy - When Science and Society Meet around Energy Matters, Liège, Belgium.

Castronovo, M., Ernst, D., & Fonteneau, R. (2014). Bayes Adaptive Reinforcement Learning versus Off-line Prior-based Policy Search: an Empirical Comparison. In Proceedings of the 23rd annual machine learning conference of Belgium and the Netherlands (BENELEARN 2014).

Castronovo, M., Ernst, D., & Fonteneau, R. (2014). Apprentissage par renforcement bayésien versus recherche directe de politique hors-ligne en utilisant une distribution a priori: comparaison empirique. In Proceedings des 9èmes Journée Francophones de Planification, Décision et Apprentissage.

François-Lavet, V., Fonteneau, R., & Ernst, D. (2014). Estimating the revenues of a hydrogen-based high-capacity storage device: methodology and results. In Proceedings des 9èmes Journée Francophones de Planification, Décision et Apprentissage.

Fonteneau, R. (2014). Transition énergétique : maximiser le retour énergétique à long terme. Paper presented at Sustainable Energy Course, Liège, Belgium.

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2014). Lipschitz robust control from off-policy trajectories. In Proceedings of the 53rd IEEE Conference on Decision and Control (IEEE CDC 2014).

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2014). Apprentissage par renforcement batch fondé sur la reconstruction de trajectoires artificielles. In Proceedings of the 9èmes Journées Francophones de Planification, Décision et Apprentissage (JFPDA 2014).

Fonteneau, R., & Prashanth L.A. (2014). Simultaneous perturbation algorithms for batch off-policy search. In Proceedings of the 53rd IEEE Conference on Decision and Control (IEEE CDC 2014).

Fonteneau, R. (2013). Min Max Generalization for Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes. Paper presented at Dutch-Belgian Reinforcement Learning Workshop, Maastricht, Netherlands.

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (September 2013). Batch mode reinforcement learning based on the synthesis of artificial trajectories. Annals of Operations Research, 208 (1), 383-416. doi:10.1007/s10479-012-1248-5

Fonteneau, R., Busoniu, L., & Munos, R. (2013). Planification Optimiste dans les Processus Décisionnels de Markov avec Croyance. In 8èmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA'13).

Fonteneau, R., Busoniu, L., & Munos, R. (2013). Optimistic Planning for Belief-Augmented Markov Decision Processes. In Proceedings 2013 Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-13), Singapore, 15–19 April 2013.

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2013). Min max generalization for deterministic batch mode reinforcement learning: relaxation schemes. SIAM Journal on Control and Optimization, 51 (5), 3355–3385. doi:10.1137/120867263

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2013). Généralisation Min Max pour l'Apprentissage par Renforcement Batch et Déterministe : Relaxations pour le Cas Général T Etapes. In 8èmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA'13).

Fonteneau, R., Korda, N., & Munos, R. (2013). An Optimistic Posterior Sampling Strategy for Bayesian Reinforcement Learning. In NIPS 2013 Workshop on Bayesian Optimization (BayesOpt2013).

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2013). Stratégies d'échantillonnage pour l'apprentissage par renforcement batch. Revue d'Intelligence Artificielle, 27 (2), 171-194. doi:10.3166/RIA.27.171-194

Kedenburg, G., Fonteneau, R., & Munos, R. (2013). Aggregating Optimistic Planning Trees for Solving Markov Decision Processes. In Advances in Neural Information Processing Systems 26 (2013) (pp. 2382-2390).

Fonteneau, R. (2012). Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories. Paper presented at Winter Meeting of the Canadian Mathematical Society, Montreal, Canada.

Maes, F., Fonteneau, R., Wehenkel, L., & Ernst, D. (2012). Policy search in a space of simple closed-form formulas: towards interpretability of reinforcement learning. In Discovery Science 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings (pp. 37-51). Berlin, Germany: Springer. doi:10.1007/978-3-642-33492-4_6

Fonteneau, R. (2012). Min Max Generalization for Deterministic Batch Mode Reinforcement Learning. Paper presented at Sequel Seminar, Lille, France.

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2012). Généralisation min max pour l'apprentissage par renforcement batch et déterministe : schémas de relaxation. In Septièmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA 2012).

Castronovo, M., Maes, F., Fonteneau, R., & Ernst, D. (2012). Learning exploration/exploitation strategies for single trajectory reinforcement learning. In Proceedings of the 10th European Workshop on Reinforcement Learning (EWRL 2012) (pp. 1-9).

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2012). Min max generalization for two-stage deterministic batch mode reinforcement learning: relaxation schemes.

Gemine, Q., Safadi, F., Fonteneau, R., & Ernst, D. (2012). Imitative Learning for Real-Time Strategy Games. In Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (pp. 424-429).

Fonteneau, R., Ernst, D., Boigelot, B., & Louveaux, Q. (2011). Relaxation schemes for min max generalization in deterministic batch mode reinforcement learning. In 4th International NIPS Workshop on Optimization for Machine Learning (OPT 2011).

Safadi, F., Fonteneau, R., & Ernst, D. (2011). Artificial intelligence design for real-time strategy games. In NIPS Workshop on Decision Making with Multiple Imperfect Decision Makers.

Fonteneau, R. (2011). Recent Advances in Batch Mode Reinforcement Learning: Synthesizing Artificial Trajectories. Paper presented at Grascomp's Day, Bruxelles, Belgium.

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (June 2011). Estimation Monte Carlo sans modèle de politiques de décision. Revue d'Intelligence Artificielle, 25, 321-343. doi:10.3166/ria.25.321-343

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2011). Apprentissage actif par modification de la politique de décision courante. In Sixièmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA 2011).

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2011). Active exploration by searching for experiments that falsify the computed control policy. In Proceedings of the 2011 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-11). doi:10.1109/ADPRL.2011.5967364

Fonteneau, R. (2011). Contributions to Batch Mode Reinforcement Learning. Unpublished doctoral thesis, ULiège - Université de Liège.
Jury: Ernst, D. (Promotor), Wehenkel, L. (Promotor), Louveaux, Q., Sepulchre, R., Munos, R., Murphy, S., ... Sebag, M.

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2011). Towards min max generalization in reinforcement learning. In J. Filipe, A. Fred, ... B. Sharp (Eds.), Agents and Artificial Intelligence: International Conference, ICAART 2010, Valencia, Spain, January 2010, Revised Selected Papers (pp. 61-77). Springer.

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Model-free Monte Carlo–like policy evaluation. In Proceedings of Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010.

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Model-free Monte Carlo-like policy evaluation. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (pp. 217-224).

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Generating informative trajectories by using bounds on the return of control policies. In Proceedings of the Workshop on Active Learning and Experimental Design 2010 (in conjunction with AISTATS 2010).

Fonteneau, R., & Ernst, D. (2010). Voronoi model learning for batch mode reinforcement learning. University of Liège.

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2010). Model-free Monte Carlo-like policy evaluation. In 29th Benelux Meeting on Systems and Control.

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2010). Computing bounds for kernel-based policy evaluation in reinforcement learning. University of Liège.

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). A cautious approach to generalization in reinforcement learning. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence (pp. 10).

Mhawej, M.-J., Brunet-Francois, C., Fonteneau, R., Ernst, D., Ferré, V., Stan, G.-B., Raffi, F., & Moog, C. H. (July 2009). Apoptosis characterizes immunological failure of HIV infected patients. Control Engineering Practice, 17 (7), 798-804. doi:10.1016/j.conengprac.2009.01.001

Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2009). Inferring bounds on the performance of a control policy from a sample of trajectories. In Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-09) (pp. 117-123). doi:10.1109/ADPRL.2009.4927534

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2009). Inferring bounds on the performance of a control policy from a sample of one-step system transitions. In 28th Benelux Meeting on Systems and Control.

Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2009). Dynamic treatment regimes using reinforcement learning: a cautious generalization approach. Poster session presented at Benelux Bioinformatics Conference (BBC 2009), Liège, Belgium.

Fonteneau, R., Wehenkel, L., & Ernst, D. (2008). Variable selection for dynamic treatment regimes: a reinforcement learning approach. In The annual machine learning conference of Belgium and the Netherlands (BeNeLearn 2008).

Stan, G.-B., Belmudes, F., Fonteneau, R., Zeggwagh, F., Lefebvre, M.-A., Michelet, C., & Ernst, D. (March 2008). Modelling the influence of activation-induced apoptosis of CD4+ and CD8+ T-cells on the immune system response of a HIV-infected patient. IET Systems Biology, 2 (2), 94-102. doi:10.1049/iet-syb:20070029

Fonteneau, R., Wehenkel, L., & Ernst, D. (2008). Variable selection for dynamic treatment regimes: a reinforcement learning approach. Paper presented at European Workshop on Reinforcement Learning 2008 (EWRL'08), Villeneuve d'Ascq, France.

Fonteneau, R., Wehenkel, L., & Ernst, D. (2008). Variable selection for dynamic treatment regimes. In 27th Benelux Meeting on Systems and Control.

Stan, G.-B., Belmudes, F., Fonteneau, R., Zeggwagh, F., Lefebvre, M.-A., Michelet, C., & Ernst, D. (2007). Modelling the influence of activation-induced apoptosis of CD4+ and CD8+ T-cells on the immune system response of a HIV infected patient. Poster session presented at Benelux Bioinformatics Conference (BBC 2007), Leuven, Belgium.