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Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
April 2, 2024, 7:45 p.m. | Aleksei Ustimenko, Aleksandr Beznosikov
cs.LG updates on arXiv.org arxiv.org
Abstract: In this work, we consider rather general and broad class of Markov chains, Ito chains, that look like Euler-Maryama discretization of some Stochastic Differential Equation. The chain we study is a unified framework for theoretical analysis. It comes with almost arbitrary isotropic and state-dependent noise instead of normal and state-independent one as in most related papers. Moreover, in our chain the drift and diffusion coefficient can be inexact in order to cover wide range of …
abstract analysis approximation arxiv boosting class cs.lg differential differential equation diffusion equation framework general ito look markov math.oc math.pr optimization sampling stat.ml stochastic study type universal work
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