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Found 2 papers, 1 papers with code
Learning heavy-tailed distributions with Wasserstein-proximal-regularized $α$-divergences
In this paper, we propose Wasserstein proximals of $\alpha$-divergences as suitable objective functionals for learning heavy-tailed distributions in a stable manner.
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate new samples.
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