Establishes maximal concentration bounds for stochastic approximation under heavy-tailed Markovian noise, with tails ranging from sub-Gaussian to heavier than Weibull depending on step sizes and contractivity properties, plus a truncation argument for unbounded noise.
arXiv preprint arXiv:1909.00843 , year=
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Stochastic integer optimization has sample complexity that matches, undercuts, or exceeds the continuous case based on objective structure, with new tight bounds for nonconvex continuous problems.
A survey of Lyapunov techniques using generalized Moreau envelopes as universal functions for non-asymptotic mean-square convergence analysis of stochastic iterative algorithms under contractive operators.
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