| Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited OD Domingues, P Ménard, E Kaufmann, M Valko Algorithmic Learning Theory, 578-598, 2021 | 131 | 2021 |
| Fast active learning for pure exploration in reinforcement learning P Ménard, OD Domingues, A Jonsson, E Kaufmann, E Leurent, M Valko International Conference on Machine Learning, 7599-7608, 2021 | 118 | 2021 |
| Adaptive reward-free exploration E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko Algorithmic Learning Theory, 865-891, 2021 | 101 | 2021 |
| Kernel-based reinforcement learning: A finite-time analysis OD Domingues, P Ménard, M Pirotta, E Kaufmann, M Valko International Conference on Machine Learning, 2783-2792, 2021 | 55* | 2021 |
| Ucb momentum q-learning: Correcting the bias without forgetting P Ménard, OD Domingues, X Shang, M Valko International Conference on Machine Learning, 7609-7618, 2021 | 50 | 2021 |
| A kernel-based approach to non-stationary reinforcement learning in metric spaces OD Domingues, P Ménard, M Pirotta, E Kaufmann, M Valko International Conference on Artificial Intelligence and Statistics, 3538-3546, 2021 | 47 | 2021 |
| Planning in markov decision processes with gap-dependent sample complexity A Jonsson, E Kaufmann, P Ménard, O Darwiche Domingues, E Leurent, ... Advances in Neural Information Processing Systems 33, 1253-1263, 2020 | 41 | 2020 |
| Achievable rates of space-division multiplexed submarine links subject to nonlinearities and power feed constraints OD Domingues, DAA Mello, R da Silva, SÖ Arık, JM Kahn Journal of lightwave technology 35 (18), 4004-4010, 2017 | 41* | 2017 |
| rlberry - A reinforcement learning library for research and education OD Domingues, Y Flet-Berliac, E Leurent, P Ménard, X Shang, M Valko GitHub Repository, 2021 | 24 | 2021 |
| Planning in entropy-regularized Markov decision processes and games JB Grill, O Darwiche Domingues, P Ménard, R Munos, M Valko Advances in Neural Information Processing Systems 32, 2019 | 19 | 2019 |
| Density-based bonuses on learned representations for reward-free exploration in deep reinforcement learning OD Domingues, C Tallec, R Munos, M Valko ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021 | 8 | 2021 |
| Adaptive multi-goal exploration J Tarbouriech, OD Domingues, P Ménard, M Pirotta, M Valko, A Lazaric International Conference on Artificial Intelligence and Statistics, 7349-7383, 2022 | 4 | 2022 |
| Multifractal analysis for cumulant-based epileptic seizure detection in EEG time series O Domingues, P Ciuciu, D Rocca, P Abry, H Wendt IEEE 16th International Symposium on Biomedical Imaging, 2019 | 4 | 2019 |
| Retinotopy of percepts elicited by an IRIS epi-retinal implant VJ Bismuth, M Keserue, L Wagenfeld, T Schade, Y Le Mer, ... Investigative Ophthalmology & Visual Science 58 (8), 4190-4190, 2017 | 1 | 2017 |
| Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients B Schmauch, V Cabeli, OD Domingues, JE Le Douget, A Hardy, ... iScience 28 (1), 2025 | | 2025 |
| Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma L Fidon, C Thiriez, A Grimaldi, OD Domingues, C Maussion, C Hoffmann Cancer Research 84 (6_Supplement), 7395-7395, 2024 | | 2024 |
| Exploration in Reinforcement Learning: Beyond Finite State-Spaces OD Domingues Université de Lille, 2022 | | 2022 |
Pierre MénardOvGU MagdeburgE-mailová adresa ověřena na: inria.fr
