April 2, 2024, 7:45 p.m. | Aleksei Ustimenko, Aleksandr Beznosikov

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.06081v2 Announce Type: replace-cross
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

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York