| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 208 | 2018 |
| The predictron: End-to-end learning and planning D Silver, H van Hasselt, M Hessel, T Schaul, A Guez, T Harley, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 121 | 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 | 121 | 2015 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... arXiv preprint arXiv:1704.03732, 4565-4573, 2017 | 83 | 2017 |
| Text classification: A sequential reading approach G Dulac-Arnold, L Denoyer, P Gallinari European Conference on Information Retrieval, 411-423, 2011 | 32 | 2011 |
| 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 | 27 | 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 | 18 | 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 | 16 | 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 | 14 | 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 |
| Challenges of real-world reinforcement learning G Dulac-Arnold, D Mankowitz, T Hester arXiv preprint arXiv:1904.12901, 2019 | 10 | 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 | 4 | 2019 |
| 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 Paris 6, 2014 | 1 | 2014 |
| Differentiable Deep Clustering with Cluster Size Constraints A Genevay, G Dulac-Arnold, JP Vert arXiv preprint arXiv:1910.09036, 2019 | | 2019 |
| Optimizing data center controls using neural networks RA Evans, J Gao, MC Ryan, G Dulac-Arnold, JK Scholz, TA Hester US Patent App. 15/410,547, 2018 | | 2018 |
| Sequentially Generated Instance-Dependent Image Representations for Classification M Cord, N Thome, L Denoyer, G Dulac-Arnold | | 2013 |
| Classification Localement Parcimonieuse par Méthodes Séquentielles G Dulac-Arnold, L Denoyer, P Preux, P Gallinari | | 2012 |
| Apprentissage par renforcement rapide pour des grands ensembles d'actions en utilisant des codes correcteurs d'erreur G Dulac-Arnold, L Denoyer, P Preux, P Gallinari | | 2012 |
| Lecture Séquentielle de Documents pour la Classification G Dulac-Arnold, L Denoyer, P Gallinari | | 2012 |
Todd HesterResearch Scientist, Google DeepMindVerified email at google.com
