| Sylvester Normalizing Flows for Variational Inference R Berg, L Hasenclever, JM Tomczak, M Welling UAI, 2018 | 226 | 2018 |
| 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 | 129 | 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 | 119 | 2019 |
| Catch & carry: reusable neural controllers for vision-guided whole-body tasks J Merel, S Tunyasuvunakool, A Ahuja, Y Tassa, L Hasenclever, V Pham, ... ACM Transactions on Graphics (TOG) 39 (4), 39: 1-39: 12, 2020 | 102 | 2020 |
| Information asymmetry in KL-regularized RL A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ... International Conference on Learning Representations, 2018 | 92 | 2018 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 84 | 2022 |
| Mix & match agent curricula for reinforcement learning W Czarnecki, S Jayakumar, M Jaderberg, L Hasenclever, YW Teh, ... International Conference on Machine Learning, 1087-1095, 2018 | 79 | 2018 |
| Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ... The Journal of Machine Learning Research 18 (1), 3744-3780, 2017 | 75* | 2017 |
| A distributional view on multi-objective policy optimization A Abdolmaleki, S Huang, L Hasenclever, M Neunert, F Song, M Zambelli, ... International conference on machine learning, 11-22, 2020 | 60 | 2020 |
| Observational learning by reinforcement learning D Borsa, B Piot, R Munos, O Pietquin arXiv preprint arXiv:1706.06617, 2017 | 60 | 2017 |
| The true cost of stochastic gradient Langevin dynamics T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ... arXiv preprint arXiv:1706.02692, 2017 | 55 | 2017 |
| Relativistic Monte Carlo X Lu, V Perrone, L Hasenclever, YW Teh, SJ Vollmer AISTATS, 2017 | 44 | 2017 |
| 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 | 41 | 2019 |
| CoMic: Complementary task learning & mimicry for reusable skills L Hasenclever, F Pardo, R Hadsell, N Heess, J Merel International Conference on Machine Learning, 4105-4115, 2020 | 40 | 2020 |
| Language to Rewards for Robotic Skill Synthesis W Yu, N Gileadi, C Fu, S Kirmani, KH Lee, MG Arenas, HTL Chiang, ... arXiv preprint arXiv:2306.08647, 2023 | 34 | 2023 |
| Behavior priors for efficient reinforcement learning D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ... The Journal of Machine Learning Research 23 (1), 9989-10056, 2022 | 28 | 2022 |
| Lateral controls on grounding-line dynamics SS Pegler, KN Kowal, LQ Hasenclever, MG Worster Journal of Fluid Mechanics 722, R1, 2013 | 28 | 2013 |
| Divide-and-conquer monte carlo tree search for goal-directed planning G Parascandolo, L Buesing, J Merel, L Hasenclever, J Aslanides, ... arXiv preprint arXiv:2004.11410, 2020 | 27 | 2020 |
| An investigation into irreducible autocatalytic sets and power law distributed catalysis W Hordijk, L Hasenclever, J Gao, D Mincheva, J Hein Natural Computing 13, 287-296, 2014 | 23 | 2014 |
| Learning dynamics models for model predictive agents M Lutter, L Hasenclever, A Byravan, G Dulac-Arnold, P Trochim, N Heess, ... arXiv preprint arXiv:2109.14311, 2021 | 20 | 2021 |
