Papers › How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

4 Feb 2023arXiv:2302.02169archive 2025-07-28

Jinghan Yang, Sarthak Jain, Byron C. Wallace

We consider the problem of identifying a minimal subset of training data 𝒮ₜ such that if the instances comprising 𝒮ₜ had been removed prior to training, the categorization of a given test point xₜ would have been different. Identifying such a set may be of interest for a few reasons. First, the cardinality of 𝒮ₜ provides a measure of robustness (if |𝒮ₜ| is small for xₜ, we might be less confident in the corresponding prediction), which we show is correlated with but complementary to predicted probabilities. Second, interrogation of 𝒮ₜ may provide a novel mechanism for contesting a particular model prediction: If one can make the case that the points in 𝒮ₜ are wrongly labeled or irrelevant, this may argue for overturning the associated prediction. Identifying 𝒮ₜ via brute-force is intractable. We propose comparatively fast approximation methods to find 𝒮ₜ based on influence functions, and find that -- for simple convex text classification models -- these approaches can often successfully identify relatively small sets of training examples which, if removed, would flip the prediction.

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