The reading group is open to anyone who is interested in machine learning and who wants to meet up regularly to discuss ML research papers. If you want to get added to the mailing list or have any other questions feel free to contact Martin Andrae.
For HT 2026 we will continue with the format where we stick to papers on the same topic for two subsequent sessions. The first session (Aug 26) will be devoted to deciding all the topics for the fall.
- One or two people are designated the host for each topic (two sessions). They are responsible for choosing the papers to be discussed and for leading the discussion during the session.
- We meet Wednesdays 11.00-12:00 on odd weeks. (Change from previous years)
- We will meet in Thomas Bayes, B-building (map).
- To stimulate discussions we ask you to write down two observations about the paper and bring them to the session. This could be something you liked, disliked, did not understand, a connection you made or something else entirely.
- Choose one paper for each session related to your topic that you think would be interesting to discuss in the reading group. In order to set a focus for the reading group we came up with the following short guidelines for how to choose papers:
- The main topic of the paper should be core machine learning research. Try to avoid papers that just apply well known machine learning methods to specific application areas.
- Make sure the paper is of high quality. Read through it yourself and try to gauge its quality. As a guideline, think that it should be publishable at a top machine learning conference (i.e. the paper should be of such quality, it does not have to actually be a short conference paper).
- If you want a second opinion on whether a paper is suitable feel free to ask anyone who has been in the reading group previous years.
- Think about how the two papers in your topic relate to each other. For example, it can be nice to discuss first an introductory paper and then the state-of-the-art, or two different approaches to/perspectives on the same underlying problem.
- Send out a link to the paper on the mailing list at least one week in advance.
- As the host it is also good to somewhat lead the discussion during the session. If you want you can give a short description of why you chose this paper, but there is no need for any proper presentation. It might be a good idea to come to the session prepared with a few discussion points, just to keep the conversation going.
Week 35 (Aug 26)
Decide on topics and format for the fall.
Week 37 (Sep 9)
Topic: Hot topics: World models
- Host: Lisa, Erik W
LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
Randall Balestriero & Yann LeCun
https://arxiv.org/abs/2511.08544
Week 39 (Sep 23)
Topic: Explainable AI
- Host: Martin, Marc
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho
https://arxiv.org/abs/2006.11287
Our rating: 3 ± 0.83
Week 41 (Oct 7)
Topic: Gaussian Splatting
- Host: Martin
Scaling Density Functional Theory with Gaussian Splatting
Andrés Guzmán-Cordero, Cindy Zhang, Majdi Hassan, Marta Skreta, Kirill Neklyudov, Matija Medvidović
https://arxiv.org/abs/2609.31483
Week 43 (Oct 21)
Location TBA
Topic: Quantum Machine Learning
- Host: Martin, Erik R
Week 45 (Nov 4)
Topic: Gaussian processes
- Host: Adi, Leo
Week 47 (Nov 18)
Topic: Position papers
- Host: Erik W, Adi
Week 49 (Dec 2)
Topic: Machine Learning for point clouds
- Host: Leo, Lisa
Week 51 (Dec 16)
Topic: Hot topics: ?
- Host: ?
5: Very Strong Accept:
- Technically flawless paper
- with groundbreaking impact on at least one area of ML and excellent impact on multiple areas of ML,
- with flawless evaluation, resources, and reproducibility,
- and no unaddressed ethical considerations.
4: Strong Accept:
- Technically strong paper, with novel ideas,
- excellent impact on at least one area of ML or high-to-excellent impact on multiple areas of ML,
- with excellent evaluation, resources, and reproducibility,
- and no unaddressed ethical considerations.
3: Accept:
- Technically solid paper,
- with high impact on at least one sub-area of ML or moderate-to-high impact on more than one area of ML,
- with good-to-excellent evaluation, resources, reproducibility,
- and no unaddressed ethical considerations.
2: Weak Accept:
- Technically solid,
- moderate-to-high impact paper,
- with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
1: Borderline accept:
- Technically solid paper
- where reasons to accept outweigh reasons to reject, e.g., limited evaluation.