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$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications.
Multilevel Monte Carlo Variational Inference
We theoretically show that, with our method, the variance of the gradient estimator decreases as optimization proceeds and that a learning rate scheduler function helps improve the convergence.
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