Gabriel Dulac-Arnold
Gabriel Dulac-Arnold
Google Research
Verified email at blacksheep.ai
Title
Cited by
Cited by
Year
Deep q-learning from demonstrations
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ...
arXiv preprint arXiv:1704.03732, 2017
3832017
Deep reinforcement learning in large discrete action spaces
G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ...
arXiv preprint arXiv:1512.07679, 2015
2272015
The predictron: End-to-end learning and planning
D Silver, H Hasselt, M Hessel, T Schaul, A Guez, T Harley, ...
International Conference on Machine Learning, 3191-3199, 2017
1752017
Learning from demonstrations for real world reinforcement learning
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ...
1232017
Challenges of real-world reinforcement learning
G Dulac-Arnold, D Mankowitz, T Hester
arXiv preprint arXiv:1904.12901, 2019
972019
Datum-wise classification: a sequential approach to sparsity
G Dulac-Arnold, L Denoyer, P Preux, P Gallinari
Joint European Conference on Machine Learning and Knowledge Discovery iná…, 2011
362011
Text classification: A sequential reading approach
G Dulac-Arnold, L Denoyer, P Gallinari
European Conference on Information Retrieval, 411-423, 2011
352011
Sequential approaches for learning datum-wise sparse representations
G Dulac-Arnold, L Denoyer, P Preux, P Gallinari
Machine learning 89 (1-2), 87-122, 2012
192012
Sequentially generated instance-dependent image representations for classification
G Dulac-Arnold, L Denoyer, N Thome, M Cord, P Gallinari
arXiv preprint arXiv:1312.6594, 2013
182013
Deep reinforcement learning with attention for slate Markov decision processes with high-dimensional states and actions
P Sunehag, R Evans, G Dulac-Arnold, Y Zwols, D Visentin, B Coppin
arXiv preprint arXiv:1512.01124, 2015
162015
Fast reinforcement learning with large action sets using error-correcting output codes for mdp factorization
G Dulac-Arnold, L Denoyer, P Preux, P Gallinari
Joint European Conference on Machine Learning and Knowledge Discovery iná…, 2012
132012
An empirical investigation of the challenges of real-world reinforcement learning
G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ...
arXiv preprint arXiv:2003.11881, 2020
92020
Differentiable deep clustering with cluster size constraints
A Genevay, G Dulac-Arnold, JP Vert
arXiv preprint arXiv:1910.09036, 2019
72019
Deep multi-class learning from label proportions
G Dulac-Arnold, N Zeghidour, M Cuturi, L Beyer, JP Vert
arXiv preprint arXiv:1905.12909, 2019
62019
Rl unplugged: Benchmarks for offline reinforcement learning
C Gulcehre, Z Wang, A Novikov, TL Paine, SG Colmenarejo, K Zolna, ...
arXiv preprint arXiv:2006.13888, 2020
32020
Datum-wise classification
G Dulac-arnold, L Denoyer, P Preux, P Gallinari
A sequential Approach to sparsity. ECML/PKDD, 375-390, 0
2
Model-based offline planning
A Argenson, G Dulac-Arnold
arXiv preprint arXiv:2008.05556, 2020
12020
A General Sequential Model for Constrained Classification
G Dulac-Arnold
Sorbonne UniversitÚ, 2014
12014
A Geometric Perspective on Self-Supervised Policy Adaptation
C Bodnar, K Hausman, G Dulac-Arnold, R Jonschkowski
arXiv preprint arXiv:2011.07318, 2020
2020
Optimizing data center controls using neural networks
RA Evans, J Gao, MC Ryan, G Dulac-Arnold, JK Scholz, TA Hester
US Patent App. 16/863,357, 2020
2020
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Articles 1–20