Dr. François Rivest, Assistant Professor

Department of Mathematics & Computer Science

Education:

• PhD Computer Science, Université de Montréal.
• MSc Computer Science, Dean’s Honour List, McGill University.
• BSc Joint Honours in Mathematics and Computer Science, Minor in Cognitive Science, McGill University.

Research Interests:

I am interested in the mathematical foundation of artificial and natural learning. I am particularly interested in how different systems in the brain collaborate to generate its amazing learning ability. For example, the cortex, the basal ganglia and the dopaminergic system, the limbic system and the hippocampus, and the cerebellum, all have sensibly different roles in learning. Beyond its potential application in neuropsychology, unleashing the brain’s learning strategy might also help develop more general and powerful machine learning algorithms.

Animal and machine learning can both be studied under the reinforcement learning framework made of stimuli, actions and rewards. In particular, the brain receives a continuous stream of inputs in which the timing of the various events seems to strongly influence the animal learning and behaviours. How time intervals are learnt and influence learning remains unclear. Beyond timing, the brain’s ability to naturally construct abstract representations remains unreplicated by today’s best machine learning algorithms.

Starting from the animal behavioural and neurophysiological data, I try to develop models that reproduce the animal’s neural learning ability. In some cases, interesting solutions are also evaluated as potential machine learning algorithms.

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Selected Publications:

• Rivest, F. (2010) Learning and the partial observability of continuous time. 5th Barbados Workshop on Reinforcement Learning: Sculpting Representation for Reinforcement Learning. McGill University, Barbados. Invited Presentation
• Rivest, F. (2009) Modèle informatique du coapprentissage des ganglions de la base et du cortex : L’apprentissage par renforcement et le développement de représentations. Thèse de doctorat, Département d’informatique et de recherche opérationnelle, Université de Montréal.
• Rivest, F., Kalaska, J.F., & Bengio, Y. (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience 28(1):107-130. doi: 10.1007/s10827-009-0191-1
• Shultz, T.R., Rivest, F., Egri, L., Thivierge, J.-P., & Dandurand, F. (2007) Could Knowledge-based Neural Learning Be Useful in Developmental Robotics? The Case of KBCC. International Journal of Humanoid Robotics (Special Issue on Autonomous Mental Development) 4(2):245-279. doi: 10.1142/S0219843607001035
• Dandurand, F., Shultz, T.R., & Rivest, F. (2007) Complex problem solving with reinforcement learning. In Proceeding of the 6th IEEE International Conference on Development and Learning (ICDL-2007), pp. 157-162. IEEE.
• Thivierge, J.-P., Rivest, F., & Monchi, O. (2007) Spiking Neurons, Dopamine, and Plasticity: Timing Is Everything, But Concentration Also Matters. Synapse 61:375-390. doi: 10.1002/syn.20378
• Shultz, T.R., Rivest, F., Egri, L., & Thivierge, J.P. (2006) Knowledge-based learning with KBCC. Proceedings of the Fifth International Conference on Development and Learning ICDL 2006. Department of Psychological and Brain Sciences, Indiana University, Bloomington.
• Rivest, F., Bengio, Y, & Kalaska, J.F. (2005) Brain Inspired Reinforcement Learning (pdf 230KB). In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pp. 1129-1136. MIT Press, Cambridge, MA.
• Rivest, F., & Shultz, T.R. (2004) Compositionality in a Knowledge-based Constructive Learner (pdf 305KB). Papers from the 2004 AAAI Symposium, Technical Report FS-04-03, pp. 54-58. AAAI Press: Menlo Park, CA.
• Rivest, F., & Precup, D. (2003). Combining TD-learning with Cascade-correlation Networks (pdf 119KB). Proceedings of the Twentieth International Conference on Machine Learning, pp. 632-639. AAAI Press. aaai.org
• Thivierge, J.-P., Rivest, F., & Shultz, T.R. (2003). A Dual-phase Technique for Pruning Constructive Networks. Proceedings of the IEEE International Joint Conference on Neural Networks 2003, pp. 559-564.
• Rivest, F. (2002) Knowledge-Transfer in Neural Network: Knowledge-Based Cascade-Correlation (pdf 644KB). M.Sc. Thesis, School of Computer Science, McGill University.
• Rivest, F. & Shultz, T.R. (2002) Application of Knowledge-based Cascade-correlation to Vowel Recognition, IEEE International Joint Conference on Neural Network 2002, pp. 53-58. IEEE Society Press.
• Shultz, T.R. & Rivest, F. (2001) Knowledge-based Cascade-correlation: Using Knowledge to Speed Learning, Connection Science 13:1-30.  doi: 10.1080/09540090110047767
• Shultz, T.R. & Rivest, F. (2000) Knowledge-based Cascade-correlation: An Algorithm for Using Knowledge to Speed Learning. Proceedings of the Seventeenth International Conference on Machine Learning, pp. 871-878. San Francisco, CA: Morgan Kaufmann.
• Shultz, T.R. & Rivest, F. (2000) Knowledge-based Cascade-correlation, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Network 2000, pp. V641-V646. Los Alamitos, CA: IEEE Society Press.
• More publications ...