3 accepted papers
Data attribution estimates how a trained model would change if a subset of training points were removed, and is a central primitive for tasks such as interpretability, data valuation, and machine unlearning. Despite its widespread use, our theoretical understanding of key data attribution methods --…
How does the training data affect a model's behavior? This is the question we seek to answer with *data attribution*. The leading practical approaches to data attribution are based on *influence functions* (IF). IFs utilize a first-order Taylor approximation to efficiently predict the effect of remo…
It has recently been discovered that the conclusions of many highly influential econometrics studies can be overturned by removing a very small fraction of their samples (often less than $0.5\%$). These conclusions are typically based on the results of one or more Ordinary Least Squares (OLS) regres…