104 citations · 153 across the 11 of their papers we have counts for
3 papers · 1 filter
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller +1
Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inher…
PFNs4BO: In-Context Learning for Bayesian Optimization
Samuel Müller, Matthias Feurer, Noah Hollmann +1
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the poster…
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Noah Hollmann, Samuel Müller, Frank Hutter
As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing…