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2020 – today
- 2022
[c88]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-values. AISTATS 2022: 1380-1402- [c87]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c86]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS 2022: 489-497 - [c85]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable RL by Learning When to Act. AAMAS 2022: 669-677 - [c84]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling Mean Field Games by Online Mirror Descent. AAMAS 2022: 1028-1037 - [i50]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act. CoRR abs/2203.08542 (2022) - [i49]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Élie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. CoRR abs/2203.11973 (2022) - [i48]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR abs/2205.09589 (2022) - 2021
- [j16]Antoine Mahé, Antoine Richard
, Stéphanie Aravecchia, Matthieu Geist, Cédric Pradalier:
Evaluation of Prioritized Deep System Identification on a Path Following Task. J. Intell. Robotic Syst. 101(4): 78 (2021) - [j15]Othmane-Latif Ouabi, Pascal Pomarede, Matthieu Geist, Nico F. Declercq, Cédric Pradalier:
A FastSLAM Approach Integrating Beamforming Maps for Ultrasound-Based Robotic Inspection of Metal Structures. IEEE Robotics Autom. Lett. 6(2): 2908-2913 (2021) - [j14]Antoine Richard, Stéphanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cédric Pradalier:
How to Train Your HERON. IEEE Robotics Autom. Lett. 6(3): 5247-5252 (2021) - [c83]Johan Ferret, Olivier Pietquin, Matthieu Geist:
Self-Imitation Advantage Learning. AAMAS 2021: 501-509 - [c82]Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin:
Show Me the Way: Intrinsic Motivation from Demonstrations. AAMAS 2021: 620-628 - [c81]Antoine Richard, Stéphanie Aravecchia, Matthieu Geist, Cédric Pradalier:
Learning Behaviors through Physics-driven Latent Imagination. CoRL 2021: 1190-1199 - [c80]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR 2021 - [c79]Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
Primal Wasserstein Imitation Learning. ICLR 2021 - [c78]Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
Adversarially Guided Actor-Critic. ICLR 2021 - [c77]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. ICML 2021: 2307-2318 - [c76]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphaël Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. ICML 2021: 4511-4522 - [c75]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. IJCAI 2021: 356-362 - [c74]Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. NeurIPS 2021: 1898-1911 - [c73]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? NeurIPS 2021: 14656-14668 - [c72]Esther Derman, Matthieu Geist, Shie Mannor:
Twice regularized MDPs and the equivalence between robustness and regularization. NeurIPS 2021: 22274-22287 - [i47]Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
Adversarially Guided Actor-Critic. CoRR abs/2102.04376 (2021) - [i46]Antoine Richard, Stéphanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cédric Pradalier:
How To Train Your HERON. CoRR abs/2102.10357 (2021) - [i45]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling up Mean Field Games with Online Mirror Descent. CoRR abs/2103.00623 (2021) - [i44]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. CoRR abs/2103.01948 (2021) - [i43]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. CoRR abs/2105.07933 (2021) - [i42]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphaël Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. CoRR abs/2105.12034 (2021) - [i41]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? CoRR abs/2106.00672 (2021) - [i40]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR abs/2106.03787 (2021) - [i39]Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. CoRR abs/2106.04480 (2021) - [i38]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-Exploration. CoRR abs/2106.06431 (2021) - [i37]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A functional mirror ascent view of policy gradient methods with function approximation. CoRR abs/2108.05828 (2021) - [i36]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-Values. CoRR abs/2108.07041 (2021) - [i35]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. CoRR abs/2109.09717 (2021) - [i34]Thibault Lahire, Matthieu Geist, Emmanuel Rachelson:
Large Batch Experience Replay. CoRR abs/2110.01528 (2021) - [i33]Esther Derman, Matthieu Geist, Shie Mannor:
Twice regularized MDPs and the equivalence between robustness and regularization. CoRR abs/2110.06267 (2021) - [i32]Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin:
Continuous Control with Action Quantization from Demonstrations. CoRR abs/2110.10149 (2021) - 2020
- [c71]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Deep Conservative Policy Iteration. AAAI 2020: 6070-6077 - [c70]Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin:
On the Convergence of Model Free Learning in Mean Field Games. AAAI 2020: 7143-7150 - [c69]Alexis Jacq, Julien Pérolat, Matthieu Geist, Olivier Pietquin:
Foolproof Cooperative Learning. ACML 2020: 401-416 - [c68]Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. AISTATS 2020: 2529-2538 - [c67]Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
CopyCAT: : Taking Control of Neural Policies with Constant Attacks. AAMAS 2020: 548-556 - [c66]Erinc Merdivan, Sten Hanke, Matthieu Geist:
Modified Actor-Critics. AAMAS 2020: 1925-1927 - [c65]Assia Benbihi, Stéphanie Arravechia, Matthieu Geist, Cédric Pradalier:
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description. ICRA 2020: 3032-3038 - [c64]Johan Ferret, Raphaël Marinier, Matthieu Geist, Olivier Pietquin:
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning. IJCAI 2020: 2655-2661 - [c63]Othmane-Latif Ouabi, Pascal Pomarede, Matthieu Geist, Nico F. Declercq, Cédric Pradalier:
Monte-Carlo Localization on Metal Plates Based on Ultrasonic Guided Waves. ISER 2020: 345-353 - [c62]Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. NeurIPS 2020 - [c61]Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS 2020 - [c60]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. NeurIPS 2020 - [c59]Antoine Richard, Lior Fine, Offer Rozenstein, Josef Tanny, Matthieu Geist, Cédric Pradalier:
Filling Gaps in Micro-meteorological Data. ECML/PKDD (5) 2020: 101-117 - [i31]Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of Regularization in RL. CoRR abs/2003.14089 (2020) - [i30]Daoming Lyu, Bo Liu, Matthieu Geist, Wen Dong, Saad Biaz, Qi Wang:
Stable and Efficient Policy Evaluation. CoRR abs/2006.03978 (2020) - [i29]Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
Primal Wasserstein Imitation Learning. CoRR abs/2006.04678 (2020) - [i28]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. CoRR abs/2006.05990 (2020) - [i27]Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin:
Show me the Way: Intrinsic Motivation from Demonstrations. CoRR abs/2006.12917 (2020) - [i26]Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. CoRR abs/2007.03458 (2020) - [i25]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. CoRR abs/2007.14430 (2020) - [i24]Johan Ferret, Olivier Pietquin, Matthieu Geist:
Self-Imitation Advantage Learning. CoRR abs/2012.11989 (2020)
2010 – 2019
- 2019
- [j13]Daoming Lyu, Bo Liu, Matthieu Geist, Wen Dong, Saad Biaz, Qi Wang:
Stable and Efficient Policy Evaluation. IEEE Trans. Neural Networks Learn. Syst. 30(6): 1831-1840 (2019) - [c58]Anush Manukyan, Miguel A. Olivares-Méndez, Matthieu Geist, Holger Voos:
Deep Reinforcement Learning-based Continuous Control for Multicopter Systems. CoDIT 2019: 1876-1881 - [c57]Antoine Mahé, Antoine Richard, Benjamin Mouscadet, Cédric Pradalier, Matthieu Geist:
Importance Sampling for Deep System Identification. ICAR 2019: 43-48 - [c56]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
ELF: Embedded Localisation of Features in Pre-Trained CNN. ICCV 2019: 7939-7948 - [c55]Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
A Theory of Regularized Markov Decision Processes. ICML 2019: 2160-2169 - [c54]Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin:
Learning from a Learner. ICML 2019: 2990-2999 - [c53]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
Semi-supervised Domain Adaptation with Representation Learning for Semantic Segmentation Across Time. ICONIP (5) 2019: 459-466 - [c52]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
Learning Sensor Placement from Demonstration for UAV networks. ISCC 2019: 1-6 - [c51]Erinc Merdivan, Anastasios Vafeiadis, Dimitrios Kalatzis, Sten Hanke, Joahannes Kroph, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, Raouf Hamzaoui, Matthieu Geist:
Image-Based Text Classification using 2D Convolutional Neural Networks. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2019: 144-149 - [i23]Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
A Theory of Regularized Markov Decision Processes. CoRR abs/1901.11275 (2019) - [i22]Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations. CoRR abs/1905.12282 (2019) - [i21]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Deep Conservative Policy Iteration. CoRR abs/1906.09784 (2019) - [i20]Alexis Jacq, Julien Pérolat, Matthieu Geist, Olivier Pietquin:
Foolproof Cooperative Learning. CoRR abs/1906.09831 (2019) - [i19]Lucas Beyer, Damien Vincent, Olivier Teboul, Sylvain Gelly, Matthieu Geist, Olivier Pietquin:
MULEX: Disentangling Exploitation from Exploration in Deep RL. CoRR abs/1907.00868 (2019) - [i18]Erinc Merdivan, Sten Hanke, Matthieu Geist:
Modified Actor-Critics. CoRR abs/1907.01298 (2019) - [i17]Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin:
Approximate Fictitious Play for Mean Field Games. CoRR abs/1907.02633 (2019) - [i16]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
ELF: Embedded Localisation of Features in pre-trained CNN. CoRR abs/1907.03261 (2019) - [i15]Johan Ferret, Raphaël Marinier, Matthieu Geist, Olivier Pietquin:
Credit Assignment as a Proxy for Transfer in Reinforcement Learning. CoRR abs/1907.08027 (2019) - [i14]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
On Connections between Constrained Optimization and Reinforcement Learning. CoRR abs/1910.08476 (2019) - [i13]Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. CoRR abs/1910.09322 (2019) - [i12]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description. CoRR abs/1910.12468 (2019) - 2018
- [c50]Ismini Psychoula, Erinc Merdivan, Deepika Singh, Liming Chen, Feng Chen, Sten Hanke, Johannes Kropf, Andreas Holzinger, Matthieu Geist:
A Deep Learning Approach for Privacy Preservation in Assisted Living. PerCom Workshops 2018: 710-715 - [i11]Ismini Psychoula, Erinc Merdivan, Deepika Singh, Liming Chen, Feng Chen, Sten Hanke, Johannes Kropf, Andreas Holzinger, Matthieu Geist:
A Deep Learning Approach for Privacy Preservation in Assisted Living. CoRR abs/1802.09359 (2018) - [i10]Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, Andreas Holzinger:
Human Activity Recognition using Recurrent Neural Networks. CoRR abs/1804.07144 (2018) - [i9]Assia Benbihi, Matthieu Geist, Cédric Pradalier:
Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation. CoRR abs/1805.04141 (2018) - [i8]Matthieu Geist, Bruno Scherrer:
Anderson Acceleration for Reinforcement Learning. CoRR abs/1809.09501 (2018) - [i7]Erinc Merdivan, Anastasios Vafeiadis, Dimitrios Kalatzis, Sten Hanke, Johannes Kropf, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, Raouf Hamzaoui, Matthieu Geist:
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks. CoRR abs/1810.10401 (2018) - 2017
- [j12]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 28(8): 1814-1826 (2017) - [c49]Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, Andreas Holzinger:
Human Activity Recognition Using Recurrent Neural Networks. CD-MAKE 2017: 267-274 - [c48]Matthieu Geist, Bilal Piot, Olivier Pietquin:
Is the Bellman residual a bad proxy? NIPS 2017: 3205-3214 - [c47]Erinc Merdivan, Mohammad Reza Loghmani, Matthieu Geist:
Reconstruct & Crush Network. NIPS 2017: 4548-4556 - 2016
- [c46]Layla El Asri, Bilal Piot, Matthieu Geist, Romain Laroche, Olivier Pietquin:
Score-based Inverse Reinforcement Learning. AAMAS 2016: 457-465 - [c45]Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
Softened Approximate Policy Iteration for Markov Games. ICML 2016: 1860-1868 - [i6]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Difference of Convex Functions Programming Applied to Control with Expert Data. CoRR abs/1606.01128 (2016) - [i5]Matthieu Geist, Bilal Piot, Olivier Pietquin:
Should one minimize the expected Bellman residual or maximize the mean value? CoRR abs/1606.07636 (2016) - 2015
- [j11]Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon, Boris Lesner, Matthieu Geist:
Approximate modified policy iteration and its application to the game of Tetris. J. Mach. Learn. Res. 16: 1629-1676 (2015) - [j10]Matthieu Geist:
Soft-max boosting. Mach. Learn. 100(2-3): 305-332 (2015) - [j9]Bruno Scherrer, Matthieu Geist:
Recherche locale de politique dans un espace convexe. Rev. d'Intelligence Artif. 29(6): 685-704 (2015) - [c44]Deepika Singh, Erinc Merdivan, Sten Hanke, Johannes Kropf, Matthieu Geist, Andreas Holzinger:
Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment. BIRS-IMLKE 2015: 194-205 - [c43]Bilal Piot, Olivier Pietquin, Matthieu Geist:
Imitation Learning Applied to Embodied Conversational Agents. MLIS@ICML 2015: 1-5 - [c42]Thibaut Munzer, Bilal Piot, Matthieu Geist, Olivier Pietquin, Manuel Lopes:
Inverse Reinforcement Learning in Relational Domains. IJCAI 2015: 3735-3741 - 2014
- [j8]Matthieu Geist, Bruno Scherrer:
Off-policy learning with eligibility traces: a survey. J. Mach. Learn. Res. 15(1): 289-333 (2014) - [c41]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Boosted and reward-regularized classification for apprenticeship learning. AAMAS 2014: 1249-1256 - [c40]Bilal Piot, Olivier Pietquin, Matthieu Geist:
Predicting when to laugh with structured classification. INTERSPEECH 2014: 1786-1790 - [c39]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Difference of Convex Functions Programming for Reinforcement Learning. NIPS 2014: 2519-2527 - [c38]Bruno Scherrer, Matthieu Geist:
Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search. ECML/PKDD (3) 2014: 35-50 - [c37]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Boosted Bellman Residual Minimization Handling Expert Demonstrations. ECML/PKDD (2) 2014: 549-564 - [i4]Matthieu Geist, Olivier Pietquin:
Kalman Temporal Differences. CoRR abs/1406.3270 (2014) - 2013
- [j7]Hervé Frezza-Buet, Matthieu Geist:
A C++ template-based reinforcement learning library: fitting the code to the mathematics. J. Mach. Learn. Res. 14(1): 625-628 (2013) - [j6]Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin:
Classification structurée pour l'apprentissage par renforcement inverse. Rev. d'Intelligence Artif. 27(2): 155-169 (2013) - [j5]Matthieu Geist, Olivier Pietquin:
Algorithmic Survey of Parametric Value Function Approximation. IEEE Trans. Neural Networks Learn. Syst. 24(6): 845-867 (2013) - [c36]Radoslaw Niewiadomski, Jennifer Hofmann, Jérôme Urbain, Tracey Platt, Johannes Wagner, Bilal Piot, Hüseyin Çakmak, Sathish Pammi, Tobias Baur, Stéphane Dupont, Matthieu Geist, Florian Lingenfelser, Gary McKeown, Olivier Pietquin, Willibald Ruch:
Laugh-aware virtual agent and its impact on user amusement. AAMAS 2013: 619-626 - [c35]Lucie Daubigney, Matthieu Geist, Olivier Pietquin:
Random projections: A remedy for overfitting issues in time series prediction with echo state networks. ICASSP 2013: 3253-3257 - [c34]Lucie Daubigney, Matthieu Geist, Olivier Pietquin:
Particle swarm optimisation of spoken dialogue system strategies. INTERSPEECH 2013: 470-474 - [c33]Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin:
A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning. ECML/PKDD (1) 2013: 1-16 - [c32]Bilal Piot, Matthieu Geist, Olivier Pietquin:
Learning from Demonstrations: Is It Worth Estimating a Reward Function? ECML/PKDD (1) 2013: 17-32 - [c31]Lucie Daubigney, Matthieu Geist, Olivier Pietquin:
Model-free POMDP optimisation of tutoring systems with echo-state networks. SIGDIAL Conference 2013: 102-106 - [i3]Matthieu Geist, Bruno Scherrer:
Off-policy Learning with Eligibility Traces: A Survey. CoRR abs/1304.3999 (2013) - [i2]Bruno Scherrer, Matthieu Geist:
Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee. CoRR abs/1306.1520 (2013) - 2012
- [j4]Lucie Daubigney, Matthieu Geist, Senthilkumar Chandramohan, Olivier Pietquin:
A Comprehensive Reinforcement Learning Framework for Dialogue Management Optimization. IEEE J. Sel. Top. Signal Process. 6(8): 891-902 (2012) - [c30]Senthilkumar Chandramohan, Matthieu Geist, Fabrice Lefèvre, Olivier Pietquin:
Behavior Specific User Simulation in Spoken Dialogue Systems. ITG Conference on Speech Communication 2012: 1-4 - [c29]Jérémy Fix, Matthieu Geist:
Monte-Carlo Swarm Policy Search. ICAISC (SIDE-EC) 2012: 75-83 - [c28]