1 code implementation • 27 May 2023 • Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO).
6 code implementations • 5 Jul 2022 • Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.
1 code implementation • ICLR 2022 • Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter
Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points.