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

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

  • 25.4.2024 Paper discussion (who?): Messeri, Crocket:Artificial intelligence and illusions of understanding in scientific research. Nature 2024. link
  • 2.5.2024 13:00, Glassroom first (!) floor: Paper discussion (who?) The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness Yifan Hao, Tong Zhang, link
  • 9.5.2024 no reading group (public holiday)
  • 16.5.2024 tba; would need to meet in the morning (or without Ulrike)
  • 23.5.2024 no reading group (Pfingstferien and NeurIPS deadline)
  • 30.5.2024 no reading group (public holiday)
  • 6.6.2024 tba
  • 13.6.2024 tba
  • 20.6.2024 10:30, Glassroom first (!) floor: Paper discussion (who?): A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning, Alicia Curth, Alan Jeffares, Mihaela van der Schaar, Neurips 2023 pdf. We will have some guests from the economics department who are also interested in this paper.
    • su

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.
  • 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)
  • On-Demand Sampling: Learning Optimally from Multiple Distributions. by Nika Haghtalab, Michael Jordan, Eric Zhao (NeurIPS 22) pdf (Moritz)
  • 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)