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.
Do GANs always have Nash equilibria? InProceedings of the 37th International Conference on Machine Learning, volume 119, pages 3029–3039
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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.