Non-convex stochastic compositional optimization under heavy-tailed noise
This paper investigates non-convex stochastic compositional optimization under heavy-tailed noise, where the stochastic noise exhibits bounded $p$th moment with $p\in(1,2]$. The main challenges arise from biased gradient estimates of the objective and the violation of the standard bounded-variance assumption. To address these issues, we propose a generic algorithm framework of Normalized Stochastic Compositional Gradient methods … Read more