Takip et
Matthieu Geist
Matthieu Geist
Cohere (ex Google, on leave of Professor, Université de Lorraine)
univ-lorraine.fr üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
34172023
What matters for on-policy deep actor-critic methods? a large-scale study
M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ...
International conference on learning representations, 2021
509*2021
A theory of regularized markov decision processes
M Geist, B Scherrer, O Pietquin
International conference on machine learning, 2160-2169, 2019
3632019
Human activity recognition using recurrent neural networks
D Singh, E Merdivan, I Psychoula, J Kropf, S Hanke, M Geist, A Holzinger
Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12 …, 2017
2282017
IQ-Learn: Inverse soft-Q Learning for Imitation
D Garg, S Chakraborty, C Cundy, J Song, M Geist, S Ermon
arXiv preprint arXiv:2106.12142, 2022
2032022
Approximate modified policy iteration and its application to the game of Tetris.
B Scherrer, M Ghavamzadeh, V Gabillon, B Lesner, M Geist
J. Mach. Learn. Res. 16 (49), 1629-1676, 2015
1602015
On-policy distillation of language models: Learning from self-generated mistakes
R Agarwal, N Vieillard, Y Zhou, P Stanczyk, SR Garea, M Geist, ...
The Twelfth International Conference on Learning Representations, 2024
156*2024
Primal wasserstein imitation learning
R Dadashi, L Hussenot, M Geist, O Pietquin
arXiv preprint arXiv:2006.04678, 2020
1562020
Fictitious play for mean field games: Continuous time analysis and applications
S Perrin, J Pérolat, M Laurière, M Geist, R Elie, O Pietquin
Advances in neural information processing systems 33, 13199-13213, 2020
1432020
On the convergence of model free learning in mean field games
R Elie, J Perolat, M Laurière, M Geist, O Pietquin
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7143-7150, 2020
141*2020
Leverage the average: an analysis of kl regularization in reinforcement learning
N Vieillard, T Kozuno, B Scherrer, O Pietquin, R Munos, M Geist
Advances in Neural Information Processing Systems 33, 12163-12174, 2020
139*2020
Inverse reinforcement learning through structured classification
E Klein, M Geist, B Piot, O Pietquin
Advances in neural information processing systems 25, 2012
1382012
Kalman temporal differences
M Geist, O Pietquin
Journal of artificial intelligence research 39, 483-532, 2010
1282010
Algorithmic survey of parametric value function approximation
M Geist, O Pietquin
IEEE Transactions on Neural Networks and Learning Systems 24 (6), 845-867, 2013
124*2013
User simulation in dialogue systems using inverse reinforcement learning
S Chandramohan, M Geist, F Lefevre, O Pietquin
Interspeech 2011, 1025-1028, 2011
1212011
Nash learning from human feedback
R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ...
arXiv preprint arXiv:2312.00886 18, 2023
1202023
Sample-efficient batch reinforcement learning for dialogue management optimization
O Pietquin, M Geist, S Chandramohan, H Frezza-Buet
ACM Transactions on Speech and Language Processing (TSLP) 7 (3), 1-21, 2011
1202011
Bridging the gap between imitation learning and inverse reinforcement learning
B Piot, M Geist, O Pietquin
IEEE transactions on neural networks and learning systems 28 (8), 1814-1826, 2016
1182016
Munchausen reinforcement learning
N Vieillard, O Pietquin, M Geist
Advances in Neural Information Processing Systems 33, 4235-4246, 2020
1162020
Off-policy learning with eligibility traces: a survey.
M Geist, B Scherrer
J. Mach. Learn. Res. 15 (1), 289-333, 2014
1132014
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Makaleler 1–20