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401-4634-DRL 1 Credits DR D-MATH

Diffusion Models, Sampling and Stochastic Localization

Lecturers & Examiners: Prof. Dr. Yuansi Chen
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see
VVZ CR n/a

Last Updated: 2026-02-05 16:37:26

Abstract

This is a master/PhD level course to prepare students for academic research in Markov chain Monte Carlo sampling algorithms, diffusion generative models and the associated theory. We cover basic sampling algorithms, provide geometric intuitions and show frameworks for proving results in log-concave sampling.

Objective

The main objective is to learn about the basic sampling algorithms such as ball walk, Langevin algorithms and Hamiltonian Monte Carlo. From these classic sampling algorithms, we make connections with the diffusion generative models, which are the state-of-the-art models for generating natural images. Additionally, we cover theoretical frameworks to prove convergence guarantees of sampling algorithms, including the stochastic localization technique.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
DR

Examination

Type
ungraded semester performance
For doctoral students who want to obtain ECTS credits, students must hand in a short, written summary of what topics have been treated in each of the weekly lectures.

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Diffusion Models, Sampling and Stochastic Localization
  • Tue 10:15-12:00 (HG G 26.5)
14 h semesterly

Offered In