Leonard Hasenclever
Leonard Hasenclever
Research Scientist at DeepMind
Verified email at google.com - Homepage
Cited by
Cited by
Sylvester Normalizing Flows for Variational Inference
R Berg, L Hasenclever, JM Tomczak, M Welling
UAI, 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
The true cost of stochastic gradient Langevin dynamics
T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ...
arXiv preprint arXiv:1706.02692, 2017
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
Relativistic Monte Carlo
X Lu, V Perrone, L Hasenclever, YW Teh, SJ Vollmer
Mix&match-agent curricula for reinforcement learning
WM Czarnecki, SM Jayakumar, M Jaderberg, L Hasenclever, YW Teh, ...
arXiv preprint arXiv:1806.01780, 2018
Observational learning by reinforcement learning
D Borsa, B Piot, R Munos, O Pietquin
arXiv preprint arXiv:1706.06617, 2017
Lateral controls on grounding-line dynamics
SS Pegler, KN Kowal, LQ Hasenclever, MG Worster
Journal of Fluid Mechanics 722, 2013
Meta reinforcement learning as task inference
J Humplik, A Galashov, L Hasenclever, PA Ortega, YW Teh, N Heess
arXiv preprint arXiv:1905.06424, 2019
An investigation into irreducible autocatalytic sets and power law distributed catalysis
W Hordijk, L Hasenclever, J Gao, D Mincheva, J Hein
Natural Computing 13 (3), 287-296, 2014
Information asymmetry in KL-regularized RL
A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ...
International Conference on Learning Representations, 2018
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
A Distributional View on Multi-Objective Policy Optimization
A Abdolmaleki, SH Huang, L Hasenclever, M Neunert, HF Song, ...
arXiv preprint arXiv:2005.07513, 2020
Reusable neural skill embeddings for vision-guided whole body movement and object manipulation
J Merel, S Tunyasuvunakool, A Ahuja, Y Tassa, L Hasenclever, V Pham, ...
arXiv preprint arXiv:1911.06636, 2019
Variational inference with orthogonal normalizing flows
L Hasenclever, J Tomczak, R van den Berg, M Welling
Bayesian Deep Learning, NIPS 2017 workshop, 2017
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
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 App. 16/586,087, 2020
Probabilistic machine learning: methods and applications to continuous control
L Hasenclever
University of Oxford, 2018
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
L Hasenclever, F Pardo, R Hadsell, N Heess, J Merel
CoMic: Complementary Task Learning & Mimicry for Reusable Skills Supplementary Material
L Hasenclever, F Pardo, R Hadsell, N Heess, J Merel
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