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Poster
Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang · Michael Mahoney · Liam Hodgkinson

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While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present a problematic scenario when Gaussian-based variational inference fails to accurately capture tail decay. We first improve previous theory on tails of Lipschitz flows by quantifying how they affect the rate of tail decay and expanding the theory to non-Lipschitz polynomial flows. Next, we develop an alternative theory for multivariate tail parameters which is sensitive to tail-anisotropy. In doing so, we unveil a fundamental problem which plagues many existing flow-based methods: they can only model tail-isotropic distributions (i.e., distributions having the same tail parameter in every direction). To mitigate this and enable modeling of tail-anisotropic targets, we propose anisotropic tail-adaptive flows (ATAF). Experimental results confirm ATAF on both synthetic and real-world targets is competitive with prior work while also exhibiting appropriate tail-anisotropy.

Author Information

Feynman Liang (UC Berkeley)
Michael Mahoney (UC Berkeley)
Liam Hodgkinson (University of California Berkeley)

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