Doron L Bergman

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer

We present an Adversarially Pre-trained Transformer (APT) that is able to perform zero-shot meta-learning on tabular prediction tasks without using any real-world dataset to pre-train the model, extending on the recent development of Prior-Data Fitted Networks (PFNs) and TabPFN. Specifically, APT is…