| 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 | 382 | 2017 |
| 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 | 225 | 2015 |
| 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 | 172 | 2017 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 123 | 2017 |
| Challenges of real-world reinforcement learning G Dulac-Arnold, D Mankowitz, T Hester arXiv preprint arXiv:1904.12901, 2019 | 94 | 2019 |
| 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 in …, 2011 | 36 | 2011 |
| Text classification: A sequential reading approach G Dulac-Arnold, L Denoyer, P Gallinari European Conference on Information Retrieval, 411-423, 2011 | 35 | 2011 |
| 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 | 19 | 2012 |
| 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 | 18 | 2013 |
| 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 | 16 | 2015 |
| 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 in …, 2012 | 13 | 2012 |
| 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 | 8 | 2020 |
| Differentiable deep clustering with cluster size constraints A Genevay, G Dulac-Arnold, JP Vert arXiv preprint arXiv:1910.09036, 2019 | 6 | 2019 |
| Deep multi-class learning from label proportions G Dulac-Arnold, N Zeghidour, M Cuturi, L Beyer, JP Vert arXiv preprint arXiv:1905.12909, 2019 | 6 | 2019 |
| 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 | 3 | 2020 |
| Datum-wise classification G Dulac-arnold, L Denoyer, P Preux, P Gallinari A sequential Approach to sparsity. ECML/PKDD, 375-390, 0 | 2 | |
| A General Sequential Model for Constrained Classification G Dulac-Arnold Sorbonne Université, 2014 | 1 | 2014 |
| 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 |
| Model-Based Offline Planning A Argenson, G Dulac-Arnold arXiv preprint arXiv:2008.05556, 2020 | | 2020 |
Todd HesterApplied Scientist, Amazon ScoutVerified email at amazon.com
