A proxy model with adaptive constrained optimization enables non-adversarial minimization of the Jeffreys divergence, producing more stable and accurate distribution fitting than MLE or GANs especially in low-data regimes.
The frontier of simulation-based inference.Pro- ceedings of the National Academy of Sciences, 117(48):30055–30062
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Adaptive Symmetrization of the KL Divergence
A proxy model with adaptive constrained optimization enables non-adversarial minimization of the Jeffreys divergence, producing more stable and accurate distribution fitting than MLE or GANs especially in low-data regimes.