32 citations · 36 across the 4 of their papers we have counts for
3 papers · 1 filter
A Heavy-Tailed Algebra for Probabilistic Programming
Feynman Liang, Liam Hodgkinson, Michael W. Mahoney
Despite the successes of probabilistic models based on passing noise through neural networks, recent work has identified that such methods often fail to capture tail behavior accur…
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang, Liam Hodgkinson, Michael W. Mahoney
While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational infer…
Multiplicative noise and heavy tails in stochastic optimization
Liam Hodgkinson, Michael W. Mahoney
Although stochastic optimization is central to modern machine learning, the precise mechanisms underlying its success, and in particular, the precise role of the stochasticity, sti…