• 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.


  • 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.
  • X Lu, V Perrone, L Hasenclever, Y W Teh, S J Vollmer, Relativistic Monte Carlo, in AISTATS, 2017.
  • T. Nagapetyan, A. B. Duncan, L. Hasenclever, S. J. Vollmer, L. Szpruch, K. Zygalakis, The True Cost of Stochastic Gradient Langevin Dynamics, Jun-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.