| Neural probabilistic motor primitives for humanoid control J Merel, L Hasenclever, A Galashov, A Ahuja, V Pham, G Wayne, YW Teh, ... arXiv preprint arXiv:1811.11711, 2018 | 172 | 2018 |
| Meta reinforcement learning as task inference J Humplik, A Galashov, L Hasenclever, PA Ortega, YW Teh, N Heess arXiv preprint arXiv:1905.06424, 2019 | 150 | 2019 |
| Task agnostic continual learning via meta learning X He, J Sygnowski, A Galashov, AA Rusu, YW Teh, R Pascanu arXiv preprint arXiv:1906.05201, 2019 | 123 | 2019 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 115 | 2021 |
| Information asymmetry in KL-regularized RL A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ... arXiv preprint arXiv:1905.01240, 2019 | 109 | 2019 |
| Exploiting hierarchy for learning and transfer in kl-regularized rl D Tirumala, H Noh, A Galashov, L Hasenclever, A Ahuja, G Wayne, ... arXiv preprint arXiv:1903.07438, 2019 | 46 | 2019 |
| Behavior priors for efficient reinforcement learning D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ... Journal of Machine Learning Research 23 (221), 1-68, 2022 | 42 | 2022 |
| Learning dexterous manipulation from suboptimal experts R Jeong, JT Springenberg, J Kay, D Zheng, Y Zhou, A Galashov, N Heess, ... arXiv preprint arXiv:2010.08587, 2020 | 42 | 2020 |
| Meta-learning surrogate models for sequential decision making A Galashov, J Schwarz, H Kim, M Garnelo, D Saxton, P Kohli, SM Eslami, ... arXiv preprint arXiv:1903.11907, 2019 | 33 | 2019 |
| Nevis' 22: A stream of 100 tasks sampled from 30 years of computer vision research J Bornschein, A Galashov, R Hemsley, A Rannen-Triki, Y Chen, ... Journal of Machine Learning Research 24 (308), 1-77, 2023 | 22 | 2023 |
| Information theoretic meta learning with gaussian processes MK Titsias, FJR Ruiz, S Nikoloutsopoulos, A Galashov Uncertainty in Artificial Intelligence, 1597-1606, 2021 | 19 | 2021 |
| A 2-approximate algorithm to solve one problem of the family of disjoint vector subsets AE Galashov, AV Kel’manov Automation and Remote Control 75, 595-606, 2014 | 17 | 2014 |
| Learning motor primitives and training a machine learning system using a linear-feedback-stabilized policy L Hasenclever, V Pham, J Merel, A Galashov US Patent 11,403,513, 2022 | 16 | 2022 |
| Temporal difference uncertainties as a signal for exploration S Flennerhag, JX Wang, P Sprechmann, F Visin, A Galashov, ... arXiv preprint arXiv:2010.02255, 2020 | 16 | 2020 |
| Data augmentation for efficient learning from parametric experts A Galashov, JS Merel, N Heess Advances in Neural Information Processing Systems 35, 31484-31496, 2022 | 12 | 2022 |
| Continually learning representations at scale A Galashov, J Mitrovic, D Tirumala, YW Teh, T Nguyen, A Chaudhry, ... Conference on Lifelong Learning Agents, 534-547, 2023 | 8 | 2023 |
| Deep MMD gradient flow without adversarial training A Galashov, V de Bortoli, A Gretton arXiv preprint arXiv:2405.06780, 2024 | 6 | 2024 |
| Kalman filter for online classification of non-stationary data MK Titsias, A Galashov, A Rannen-Triki, R Pascanu, YW Teh, ... arXiv preprint arXiv:2306.08448, 2023 | 6 | 2023 |
| Importance Weighted Policy Learning and Adaptation A Galashov, J Sygnowski, G Desjardins, J Humplik, L Hasenclever, ... arXiv preprint arXiv:2009.04875, 2020 | 4 | 2020 |
| Towards compute-optimal transfer learning M Caccia, A Galashov, A Douillard, A Rannen-Triki, D Rao, M Paganini, ... arXiv preprint arXiv:2304.13164, 2023 | 3 | 2023 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindEmail verificata su stats.ox.ac.uk
