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

  • 6.2.2025 No regular reading group, instead on wednesday: Workshop ML meets Law
  • 13.2.2025 discussion counterexample project, please update the overleaf file
  • 20.2.2025 no reading group
  • Paper discussion (who?) Chain of Log-Concave Markov Chains, Saeed Saremi, Ji Won Park, Francis Bach, 2024 pdf
  • Paper discussion (Moritz?) Occam’s Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? pdf
  • Paper discussion (who?) GLEAMS: Bridging the Gap Between Local and Global Explanations Giorgio Visani, Vincenzo Stanzione, Damien Garreau, 2024. pdf

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)
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