Actes de Conférences :

1. D.. SINGH, E. MERDIVAN, I.. PSYCHOULA, S.. HANKE, J.. KROPF, M. GEIST, A. HOLZINGER, "Human Activity Recognition using Recurrent Neural Networks". In Cross Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), 2017.

2. A. MANUKYAN, M.A.. OLIVARES-MENDEZ, H. VOOS, M. GEIST, "Real time degradation identification of UAV using machine learning techniques". In International Conference on Unmanned Aircraft Systems (ICUAS'17), 2017.

Workshops :

1. M. GEIST, B. PIOT, O. PIETQUIN, "Faut-il minimiser le résidu de Bellman ou maximiser la valeur moyenne ?". In Journées Francophones sur la Planification, la Décision et l\'Apprentissage pour la conduite de systèmes (JFPDA), 2017.

Articles de Journaux :

1. B. PIOT, M. GEIST, O. PIETQUIN, "Bridging the Gap between Imitation Learning and Inverse Reinforcement Learning". In IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2016.

Actes de Conférences :

1. B. PIOT, M. GEIST, O. PIETQUIN, "Batch Policy Iteration Algorithms for Continuous Domains". In European Workshop on Reinforcement Learning (EWRL), 2016.

2. J. PÉROLAT, B. PIOT, M. GEIST, B. SCHERRER, O. PIETQUIN, "Softened Approximate Policy Iteration for Markov Games". In International Conference on Machine Learning (ICML), 2016.

3. L. EL ASRI, B. PIOT, M. GEIST, R. LAROCHE, O. PIETQUIN, "Score-based Inverse Reinforcement Learning". In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), 2016.

Thèses :

1. M. GEIST, "Contrôle optimal et apprentissage automatique, applications aux interactions homme-machine", 2016.

Divers :

1. M. GEIST, B. PIOT, O. PIETQUIN, "Should one minimize the expected Bellman residual or maximize the mean value?", arxiv, 2016.

2. B. PIOT, M. GEIST, O. PIETQUIN, "Difference of Convex Functions Programming Applied to Control with Expert Data", arxiv, 2016.

Articles de Journaux :

1. B. SCHERRER, M. GEIST, "Recherche locale de politique dans un espace convexe". In Revue d'Intelligence Artificielle (RIA), 29(6):685-704, 2015.

2. M. GEIST, "Soft-max boosting". In Machine Learning, 100(2):305-332, (I discovered after publication that a very similar approach has been published some time ago, see "an iterative method for multi-class cost-sensitive learning" by Abe, Zadrozny and Langford, KDD'04), 2015.

3. B. SCHERRER, M. GHAVAMZADEH, V. GABILLON, B. LESNER, M. GEIST, "Approximate Modified Policy Iteration and its Application to the Game of Tetris". In Journal of Machine Learning Research, 16:1629-1676, 2015.

Actes de Conférences :

1. M. LAUFFER, F. GENTY, S. MARGUERON, J.L. COLLETTE, J.C. PIHAN, "Automatic recognition system of aquatic organisms by classical and fluorescence microscopy". In Proceedings of SPIE 9506, 9506(1O):1-7, Prague (République Tchèque), 2015.

2. S. ROSSIGNOL, "Technique d'assistance par le bruit pour aider l'opérateur de Teager-Kaiser à suivre une composante fréquentielle perturbée par du bruit". In GRETSI, 2015.

3. B. PIOT, M. GEIST, O. PIETQUIN, "Imitation Learning Applied to Embodied Conversational Agents". In Machine Learning and Interactive Systems (MLIS), 2015.

4. M. GEIST, "A multiplicative UCB strategy for Gamma rewards". In European Workshop on Reinforcement Learning (EWRL), 2015.

5. T.. MUNZER, B. PIOT, M. GEIST, O. PIETQUIN, M.. LOPES, " Inverse Reinforcement Learning in Relational Domains". In International Joint Conferences on Artificial Intelligence (IJCAI), (to appear), 2015.

Mise à jour le 16/06/2007 par SebVL