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Mohammad Gheshlaghi Azar
Mohammad Gheshlaghi Azar
Cohere AI
Zweryfikowany adres z google.com - Strona główna
Tytuł
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Cytowane przez
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Bootstrap your own latent-a new approach to self-supervised learning
JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ...
Advances in neural information processing systems 33, 21271-21284, 2020
58362020
Rainbow: Combining improvements in deep reinforcement learning
M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ...
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
24872018
Minimax regret bounds for reinforcement learning
MG Azar, I Osband, R Munos
International conference on machine learning, 263-272, 2017
7782017
Large-scale representation learning on graphs via bootstrapping
S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ...
arXiv preprint arXiv:2102.06514, 2021
332*2021
Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model
M Gheshlaghi Azar, R Munos, HJ Kappen
Machine learning 91, 325-349, 2013
2782013
Speedy Q-Learning
MG Azar, M Ghavamzadeh, HJ Kappen, R Munos
Advances in Neural Information Processing Systems, 2411-2419, 2011
198*2011
The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning
A Gruslys, W Dabney, MG Azar, B Piot, M Bellemare, R Munos
arXiv preprint arXiv:1704.04651, 2017
164*2017
Dynamic Policy Programming
M Gheshlaghi Azar, V Gomez, HJ Kappen
Journal of Machine Learning Research 13, 3207-3245, 2012
1442012
Bootstrap latent-predictive representations for multitask reinforcement learning
ZD Guo, BA Pires, B Piot, JB Grill, F Altché, R Munos, MG Azar
International Conference on Machine Learning, 3875-3886, 2020
1382020
Observe and look further: Achieving consistent performance on atari
T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ...
arXiv preprint arXiv:1805.11593, 2018
1282018
Sequential transfer in multi-armed bandit with finite set of models
MG Azar, A Lazaric, E Brunskill
Advances in Neural Information Processing Systems, 2220-2228, 2013
1122013
On the sample complexity of reinforcement learning with a generative model
MG Azar, R Munos, B Kappen
arXiv preprint arXiv:1206.6461, 2012
1112012
Hindsight credit assignment
A Harutyunyan, W Dabney, T Mesnard, M Gheshlaghi Azar, B Piot, ...
Advances in neural information processing systems 32, 2019
892019
Neural predictive belief representations
ZD Guo, MG Azar, B Piot, BA Pires, R Munos
arXiv preprint arXiv:1811.06407, 2018
832018
Meta-learning of sequential strategies
PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ...
arXiv preprint arXiv:1905.03030, 2019
822019
Stochastic optimization of a locally smooth function under correlated bandit feedback
MG Azar, A Lazaric, E Brunskill
31st International Conference on Machine Learning (ICML), 2014
66*2014
A cryptography-based approach for movement decoding
EL Dyer, M Gheshlaghi Azar, MG Perich, HL Fernandes, S Naufel, ...
Nature biomedical engineering 1 (12), 967-976, 2017
632017
k. kavukcuoglu, R
JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ...
Munos, and M. Valko,“Bootstrap your own latent-a new approach to self …, 2020
612020
Byol-explore: Exploration by bootstrapped prediction
Z Guo, S Thakoor, M Pîslar, B Avila Pires, F Altché, C Tallec, A Saade, ...
Advances in neural information processing systems 35, 31855-31870, 2022
522022
Dynamic policy programming with function approximation
MG Azar, V Gómez, B Kappen
Proceedings of the Fourteenth International Conference on Artificial …, 2011
522011
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