Speaker: Samuel Müller
Location: Building 101, Room 00.036
Samuel Müller from the Machine Learning Lab will give a talk titled “Prior-data Fitted Networks (PFNs): Use neural networks for 100x faster Bayesian predictions“.
Bayesian methods can be expensive and complicated to approximate with e.g. Markov Chain Monte Carlo (MCMC) or Variational Inference (VI) approaches. Prior-data Fitted Networks (PFNs) are a new, cheap and simple method to accurately approximate Bayesian predictions. I will explain how to build a PFN out of a Transformer by learning to model artificial data. I present the results from our paper that introduces PFNs, in which PFNs beat VI and MCMC for some standard tasks. As well as some more recent results with our new TabPFN, where we show that a simple PFN can replace a full AutoML tool for small datasets.