How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?
Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results about the approximation quality are surprisingly thin. Taking an empirical risk minimization perspective, we decompose the deployment error of PFNs into prior, context, and...
M. Hulskamp, Julius Kobialka, Emanuel Sommer et al.
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