  We propose a general non-accelerated tensor method under inexact information
on higher-order derivatives, analyze its convergence rate, and provide
sufficient conditions for this method to have similar complexity as the exact
tensor method. As a corollary, we propose the first stochastic tensor method
for convex optimization and obtain sufficient mini-batch sizes for each
derivative.
