7 citations · 17 across the 10 of their papers we have counts for
3 papers · 1 filter
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
Kai Helli, David Schnurr, Noah Hollmann +2
While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the deg…
Bayes' Power for Explaining In-Context Learning Generalizations
Samuel Müller, Noah Hollmann, Frank Hutter
Traditionally, neural network training has been primarily viewed as an approximation of maximum likelihood estimation (MLE). This interpretation originated in a time when training…
FairPFN: Transformers Can do Counterfactual Fairness
Jake Robertson, Noah Hollmann, Noor Awad +1
Machine Learning systems are increasingly prevalent across healthcare, law enforcement, and finance but often operate on historical data, which may carry biases against certain dem…