  Many tasks in explainable machine learning, such as data valuation and
feature attribution, perform expensive computation for each data point and can
be intractable for large datasets. These methods require efficient
approximations, and learning a network that directly predicts the desired
output, which is commonly known as amortization, is a promising solution.
However, training such models with exact labels is often intractable; we
therefore explore training with noisy labels and find that this is inexpensive
and surprisingly effective. Through theoretical analysis of the label noise and
experiments with various models and datasets, we show that this approach
significantly accelerates several feature attribution and data valuation
methods, often yielding an order of magnitude speedup over existing approaches.
