Graph foundation models face several fundamental challenges including transferability across datasets and data scarcity, which calls into question the very feasibility of graph foundation models. Howe...

AI is everywhere. Can you see it here? Note Some highlights about my work in 2025: progress on tabular-learning stands out, a publication on unpacking trade-off and consequences of scale in...

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New features The DataOp.skb.full_report() now displays the time each node took to evaluate. #1596 by Jérôme Dockès. The User guide has been reworked and expanded. Changes and deprecations Ken em...

Create a regression model for a house sale price having the narrowest overall prediction intervals

This channel features videos about automated machine learning (AutoML) from the AutoML groups at the University of Freiburg, Leibniz University Hannover and University of Tübingen. Common topics inclu...

Solving partial differential equations (PDEs) is a fundamental problem in science and engineering. While neural PDE solvers can be more efficient than established numerical solvers, they often require...

Use historical product sales data to predict future sales.

Tabular data is a ubiquitous data modality due to its versatility and ease of use in many real-world applications. The predominant heuristics for handling classification tasks on tabular data rely on ...

We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedul...