Diffusion Models, Sampling and Stochastic Localization

Spring 2024

Lecturer
Yuansi Chen
Lecture Time
Tue 10:15-12:00 (from 16.04.2024 to 28.05.2024), HG G 26.5
Course Number
401-4634-24L/401-4634-DRL
Office Hours
Tue 3:00-4:00 (from 16.04.2024 to 28.05.2024), HG G 15.1

Content

Selected topics on Markov chain Monte Carlo sampling algorithms, diffusion generative models and related proof techniques.
The official course catalogue page can be found here.

Prerequisites

We assume basic knowledge of linear algebra, introduction to probability and statistics.
Familarity with convex optimization, stochastic process and stochastic calculus would help.

Lecture notes

(tentative, please refresh page)
Date Content Notes
Tu 16.04. Introduction - two settings of sampling Lecture 1
Tu 23.04. Geometric randon walks - ball walk and hit-and-run Lecture 2
Tu 30.04. Langevin diffusion and Langevin algorithms Lecture 3
Tu 07.05. Rest of ULA and intro to Diffusion models Lecture 4
Tu 14.05. Diffusion model and its convergence Lecture 5
Tu 21.05. Rest of diffusion model convergence and simulation Lecture 6
Tu 28.05. Stochastic localization and its relation to diffusion models Lecture 7

Suggested readings

Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6 Lecture 7

Extended readings and project ideas

A few vague ideas:

Literature

Acknowledgement

This course contains materials such as lecture notes/slides that were developed or adapted from many other courses. Especially,