Mathematical Machine Learning -- Generative Models and Measure Transport

Semester: 2026 WS
Class type: Seminar
Languages: English
Teachers: Roland Herzog, Georg Müller
Contact: Send e-mail
Websites for this class: MaMpf, heiCO(1100144001)

Course Information

Description

This seminar is designed for master students of Mathematics of Machine Learning and Data Science, Scientific Computing and Mathematics. We consider Machine Learning from a mathematical point of view and discuss its relation to new as well as well established mathematical concepts. This semester the focus topic is Generative Models and Measure Transport. Together we will explore various aspects of five selected papers, e.g. content, authors, references, related research, etc.

All five papers address the same problem: samples from an unknown distribution are given, and new ones are to be generated. They also solve it in the same way, by pushing a simple reference measure onto the data distribution. What separates the five papers is the notion of closeness they commit to.

The seminar style will be similar to what is described in: https://colinraffel.com/blog/role-playing-seminar.html We have selected five roles. For more details on the roles we refer to the preparatory meeting. Each participant will be assigned a number, and fill each role one time.

Papers

In every meeting we will study together one particular paper on generative models and measure transport.

  1. Kingma, Welling: Auto-encoding variational Bayes
  2. Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio: Generative adversarial nets
  3. Arjovsky, Chintala, Bottou: Wasserstein generative adversarial networks
  4. Ho, Jain, Abbeel: Denoising diffusion probabilistic models
  5. Song, Kingma, Kumar, Ermon, Poole: Score-based generative modeling through stochastic differential equations

Exam / Presentations

For a successful completion of the seminar it is mandatory to attend all sessions. In the beginning of every meeting each participant gives a five minute report from the perspective of their role, followed by a joint scientific discussion of the paper. The final grade will be a weighted sum of the grades of your presentations and of your participation in the subsequent discussions.

Prerequisites

No prior knowledge of generative models is required. Familiarity with measure theoretic probability is helpful.

Dates and Timeline

  • Organizational meeting:
    • October 13th, 2026, SR 10 of INF 205.
    • Participation in this meeting is essential to attending the seminar.
    • Roles will be assigned at this meeting.

Meetings will be held on the following Tuesdays from 16-18h at SR 10 of INF 205:

  • 27.10.2026
  • 10.11.2026
  • 24.11.2026
  • 08.12.2026
  • 12.01.2027

(Pre-)registration

Please register for this class in MaMpf . The class is limited to 10 participants. Roles will be assigned at the organizational meeting.