• Alexandre Galashov, Siddhant Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojtek M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess, Information asymmetry in KL-regularized RL, in International Conference on Learning Representations, 2019.
  • Josh Merel*, Leonard Hasenclever*, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess, Neural Probabilistic Motor Primitives for Humanoid Control, in International Conference on Learning Representations, 2019.
  • Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Remi Munos, Olivier Pietquin, Observational learning by reinforcement learning, in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019, pp. 1117–1124.
  • Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess, Exploiting Hierarchy for Learning and Transfer in KL-regularized RL, arXiv preprint arXiv:1903.07438, 2019.
  • Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A Ortega, Yee Whye Teh, Nicolas Heess, Meta reinforcement learning as task inference, arXiv preprint arXiv:1905.06424, 2019.


  • Rianne van den Berg*, Leonard Hasenclever*, Jakub M. Tomczak, Max Welling, Sylvester Normalizing Flows for Variational Inference, in UAI, 2018.
  • Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu, Mix&Match - Agent Curricula for Reinforcement Learning, in ICML, 2018.


  • T. Nagapetyan, A. B. Duncan, L. Hasenclever, S. J. Vollmer, L. Szpruch, K. Zygalakis, The True Cost of Stochastic Gradient Langevin Dynamics, Jun-2017.
  • X Lu, V Perrone, L Hasenclever, Y W Teh, S J Vollmer, Relativistic Monte Carlo, in AISTATS, 2017.
  • Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh, Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research, vol. 18, no. 106, pp. 1–37, 2017.


  • W. Hordijk, L. Hasenclever, J. Gao, D. Mincheva, J. Hein, An investigation into irreducible autocatalytic sets and power law distributed catalysis, Natural Computing, vol. 13, no. 3, pp. 287–296, 2014.


  • S. S. Pegler, K. N. Kowal, L. Hasenclever, M. G. Worster, Lateral controls on grounding-line dynamics, Journal of Fluid Mechanics, vol. 722, no. 5929, p. R1, May 2013.