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cs.LG2026
Characterizing the Separability Gap in Prior-Data Fitted Networks
Julius Kobialka, Manuel Hülskamp, Thomas Nagler +1
Prior-data fitted networks (PFNs) are transformers pretrained on simulated datasets to approximate Bayesian posterior predictive distributions. Standard PFN theory assumes a separa…
cs.LG2020★ 36 cited
Copula-based synthetic data augmentation for machine-learning emulators
David Meyer, Thomas Nagler, Robin J. Hogan
Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be…