Research Seminar "Machine Learning Theory"

This is the research seminar by Ulrike's group.

When and where

Each thursday 14:00 - 15:00, Seminar room 2rd floor, MvL6.

What

Most sessions take place in form of a reading group: everybody reads the assigned paper before the meeting. Then we jointly discuss the paper in the meeting. Sometimes we also have talks by guests or members of the group.

Who

PhD students and researchers of the University of Tübingen. We do not mind people dropping in and out depending on whether they find the current session interesting or not.

Upcoming meetings

  • 16.1.2025 Disscussing teaching next terms, and about organizing the next couple of months
  • 22.1.2025 Wednesday, 13:00-14:30!!! (Glassroom ground floor) Test talks by Robi and Moritz for Oberwolfach
  • 23.1.2025 10:30!!! (Glassroom 1rst floor) Talk by Kerstin Ritter on her work on XAI in Neuroscience
  • 30.1.2025 no reading group (many of us in Oberwolfach)
  • 6.2.2025 (colt deadline?)
  • 13.2.2025 discussion counterexample project, please update the overleaf file (last in person meeting before ulrike leaves)
  • 20.2.2025 no reading group
  • for the following weeks, we still need to fix the modalities

Past meetings

Listed here.

Suggested papers for future meetings

Feel free to make suggestions!
If you do, please (i) try to select short conference papers rather than 40-page-journal papers; (ii) please put your name when entering suggestions; it does not mean that you need to present it, but then we can judge where it comes from; (iii) Please provide a link, not just a title.
  • Ulrike: Why do random forests work? Understanding tree ensembles as self-regularizing adaptive smoothers. pdf
  • Robust Explanation for Free or At the Cost of Faithfulness. ICML 2023. link (Ulrike)
  • Trade-off Between Efficiency and Consistency for Removal-based Explanations, Neurips 2023 link (Ulrike)
  • Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning link (Ulrike)
  • Getting Aligned on Representational Alignment, 2023 pdf (David)
  • On Provable Copyright Protection for Generative Models, ICML 2023 pdf (Peru)
  • Causal Abstractions of Neural Networks, NeurIPS 2021, pdf (Gunnar)
  • A theory of interpretable approximations, COLT 2024, pdf (Gunnar)
  • Benign overfitting in ridge regression, by Alexander Tsigler and Peter Bartlett pdf
  • Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? pdf (Moritz)