  Noise plagues many numerical datasets, where the recorded values in the data
may fail to match the true underlying values due to reasons including:
erroneous sensors, data entry/processing mistakes, or imperfect human
estimates. We consider general regression settings with covariates and a
potentially corrupted response whose observed values may contain errors. By
accounting for various uncertainties, we introduced veracity scores that
distinguish between genuine errors and natural data fluctuations, conditioned
on the available covariate information in the dataset. We propose a simple yet
efficient filtering procedure for eliminating potential errors, and establish
theoretical guarantees for our method. We also contribute a new error detection
benchmark involving 5 regression datasets with real-world numerical errors (for
which the true values are also known). In this benchmark and additional
simulation studies, our method identifies incorrect values with better
precision/recall than other approaches.
