Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics
The impact of a given training point on a statistical model can be measured through its leave-one-out influence on the model parameters, which quantifies how its removal from the training set affects the learned weights. For convex M-estimation under Gaussian design, in the high-dimensional limit $n\asymp d$, we show that the empirical distribution of influences across training points concentrates around a deterministic measure which we sharply characterize. This characterization suggests that influential samples tend to lie on average close to the decision boundary, making contact with a standard data selection heuristic in active learning.
| Authors | Hugo Cui |
|---|---|
| Primary category | stat.ML |
| Published | 10 July 2026 |
| Last updated at source | 25 September 2026 |
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