Hands-On:
Next Generation AutoML Methods

We will have a hands-on tutorial on the next generation of AutoML methods.

First, we will train our own prior-data fitted networks. These are Bayesian models that show potential to be a cornerstone of models for small datasets, as shown with the TabPFN. The TabPFN, one particular PFN, is a model which is able to match performance of much more complicated and >100x slower methods on small datasets. We will also try it out to see what works and what does not.

Finally, we will look into prompting LLMs and interacting with LLMs to do feature preprocessing for us, based on our initial work on this, CAAFE.