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 3rd 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

Everybody who is interested in machine learning theory: Students, 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

  • 17.11.2022 (paper discussion, Moritz) Conformalized Quantile Regression, Yaniv Romano, Evan Patterson, Emmanuel Candes (NeurIPS 2019).
  • 23.11.2022, 11:00, (attention, unusual day and time) MvL6 Lecture hall: Talk by Tim Erven, Attribution-based Explanations that Provide Recourse Cannot be Robust.
  • 30.11. TML Day with talks and discussions
  • 8.12.2022 -- postponed ---
  • 15.12.2022 (paper discussion, Solveig) DiGress: Discrete Denoising diffusion for graph generation, Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard, pdf
  • 22.12.2022 no reading group
  • 12.1.2023 (paper discussion, who?) Understanding contrastive learning requires incorporating inductive biases, 2021 pdf
  • 19.1.2023 ? (IMPRS symposium)
  • 26.1.2023 (paper discussion, who?) pdf A Kernel-Based View of Language Model Fine-Tuning, Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora
  • 2.2.2022 (Ulrike cannot make it)
  • 9.2.2022 tba
  • 16.2.2022 tba
  • 23.2.2022 (Ulrike traveling)
  • 2.3.2023 tba
  • 9.3.2023 tba
  • 16.3.2023 tba
  • 23.3.2023 (Ulrike traveling)
  • 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.