Rahul Krishnan

D-PDDM provably monitors model deterioration requiring no training data during deployment, and performs well in real-worlds datasets.

CausalPFN is a pre-trained transformer that amortizes causal effect estimation: trained once on simulated data-generating processes, it outputs calibrated effects for new observational datasets with zero tuning.