  Likelihood-free inference is concerned with the estimation of the parameters
of a non-differentiable stochastic simulator that best reproduce real
observations. In the absence of a likelihood function, most of the existing
inference methods optimize the simulator parameters through a handcrafted
iterative procedure that tries to make the simulated data more similar to the
observations. In this work, we explore whether meta-learning can be used in the
likelihood-free context, for learning automatically from data an iterative
optimization procedure that would solve likelihood-free inference problems. We
design a recurrent inference machine that learns a sequence of parameter
updates leading to good parameter estimates, without ever specifying some
explicit notion of divergence between the simulated data and the real data
distributions. We demonstrate our approach on toy simulators, showing promising
results both in terms of performance and robustness.
