A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics. (arXiv:2202.09096v2 [cs.LG] UPDATED)

Parameter estimation in empirical fields is usually undertaken using
parametric models, and such models readily facilitate statistical inference.
Unfortunately, they are unlikely to be sufficiently flexible to be able to
adequately model real-world phenomena, and may yield biased estimates.
Conversely, non-parametric approaches are flexible but do not readily
facilitate statistical inference and may still exhibit residual bias. We
explore the potential for Influence Functions (IFs) to (a) improve initial
estimators without needing more data (b) increase model robustness and (c) …

arxiv free influence lg network neural network statistics