Recurrent machines for likelihood-free inference
This addresses the challenge of automating parameter estimation in simulators without explicit likelihoods, though it is incremental as it builds on existing meta-learning and likelihood-free methods.
The paper tackled the problem of likelihood-free inference for non-differentiable stochastic simulators by proposing a meta-learning approach that automatically learns an iterative optimization procedure, demonstrating promising results on toy simulators.
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.