2024
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
NeurIPS 2024poster
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 degradation of machine learning model performance. Until now, no tabular method has consistently outperformed classical sup…