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2020 – today
- 2023
[i61]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. CoRR abs/2301.02328 (2023)- [i60]Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Levy, Shie Mannor:
Policy Gradient for s-Rectangular Robust Markov Decision Processes. CoRR abs/2301.13589 (2023) - [i59]Pierre Clavier, Erwan Le Pennec, Matthieu Geist:
Towards Minimax Optimality of Model-based Robust Reinforcement Learning. CoRR abs/2302.05372 (2023) - [i58]Esther Derman, Yevgeniy Men, Matthieu Geist, Shie Mannor:
Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization. CoRR abs/2303.06654 (2023) - [i57]Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Léonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi:
Get Back Here: Robust Imitation by Return-to-Distribution Planning. CoRR abs/2305.01400 (2023) - [i56]Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR abs/2305.13185 (2023) - 2022
- [c96]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-exploration. AAAI 2022: 8106-8114 - [c95]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. AAAI 2022: 9413-9421 - [c94]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-values. AISTATS 2022: 1380-1402 - [c93]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 - [c92]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 - [c91]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable RL by Learning When to Act. AAMAS 2022: 669-677 - [c90]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 - [c89]Othmane-Latif Ouabi, Jiawei Yi, Neil Zeghidour, Nico F. Declercq, Matthieu Geist, Cédric Pradalier:
Polygonal Shapes Reconstruction from Acoustic Echoes Using a Mobile Sensor and Beamforming. EUSIPCO 2022: 1507-1511 - [c88]Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin:
Continuous Control with Action Quantization from Demonstrations. ICML 2022: 4537-4557 - [c87]Thibault Lahire, Matthieu Geist, Emmanuel Rachelson:
Large Batch Experience Replay. ICML 2022: 11790-11813 - [c86]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. ICML 2022: 12078-12095 - [c85]Othmane-Latif Ouabi, Ayoub Ridani, Pascal Pomarede, Neil Zeghidour, Nico F. Declercq, Matthieu Geist, Cédric Pradalier:
Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. ICRA 2022: 5056-5062 - [c84]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS 2022 - [i55]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act. CoRR abs/2203.08542 (2022) - [i54]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) - [i53]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR abs/2205.09589 (2022) - [i52]Mathieu Laurière, Sarah Perrin, Matthieu Geist, Olivier Pietquin:
Learning Mean Field Games: A Survey. CoRR abs/2205.12944 (2022) - [i51]Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári:
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR abs/2205.14211 (2022) - [i50]Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls:
Learning Correlated Equilibria in Mean-Field Games. CoRR abs/2208.10138 (2022) - [i49]Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem:
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR abs/2211.03521 (2022) - [i48]Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. CoRR abs/2212.14449 (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<