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Hessian-Aware Stochastic Differential Equation Modeling of SGD

Offered By: Erwin Schrödinger International Institute for Mathematics and Physics (ESI) via YouTube

Tags

Stochastic Gradient Descent Courses Machine Learning Courses Stochastic Differential Equation Courses

Course Description

Overview

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Explore a 29-minute conference talk from the "One World Optimization Seminar in Vienna" workshop held at the Erwin Schrödinger International Institute for Mathematics and Physics (ESI) in June 2024. Delve into the development of a novel Stochastic Differential Equation (SDE) model for Stochastic Gradient Descent (SGD) that incorporates Hessian information. Learn about the Hessian-Aware Stochastic Modified Equation (HA-SME) and its advantages over existing SDE models in capturing SGD's escaping behaviors. Discover how HA-SME achieves improved approximation error guarantees and reduced dependence on smoothness parameters. Understand the significance of HA-SME as the first SDE model to exactly recover SGD dynamics for quadratic objectives under certain conditions.

Syllabus

Niao He - A Hessian-Aware Stochastic Differential Equation Modelling of SGD


Taught by

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

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