  Simulation-based inference (SBI) enables amortized Bayesian inference for
simulators with implicit likelihoods. But when we are primarily interested in
the quality of predictive simulations, or when the model cannot exactly
reproduce the observed data (i.e., is misspecified), targeting the Bayesian
posterior may be overly restrictive. Generalized Bayesian Inference (GBI) aims
to robustify inference for (misspecified) simulator models, replacing the
likelihood-function with a cost function that evaluates the goodness of
parameters relative to data. However, GBI methods generally require running
multiple simulations to estimate the cost function at each parameter value
during inference, making the approach computationally infeasible for even
moderately complex simulators. Here, we propose amortized cost estimation (ACE)
for GBI to address this challenge: We train a neural network to approximate the
cost function, which we define as the expected distance between simulations
produced by a parameter and observed data. The trained network can then be used
with MCMC to infer GBI posteriors for any observation without running
additional simulations. We show that, on several benchmark tasks, ACE
accurately predicts cost and provides predictive simulations that are closer to
synthetic observations than other SBI methods, especially for misspecified
simulators. Finally, we apply ACE to infer parameters of the Hodgkin-Huxley
model given real intracellular recordings from the Allen Cell Types Database.
ACE identifies better data-matching parameters while being an order of
magnitude more simulation-efficient than a standard SBI method. In summary, ACE
combines the strengths of SBI methods and GBI to perform robust and
simulation-amortized inference for scientific simulators.
