Statistical and Computational Guarantees for Influence Diagnostics

Jillian Fisher; Krishna Pillutla; Lang Liu; Yejin Choi; Zaid Harchaoui

Statistical and Computational Guarantees for Influence Diagnostics

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arxiv 2212.04014 v2 pith:42SZVVCN submitted 2022-12-08 stat.ML cs.LGmath.STstat.TH

Statistical and Computational Guarantees for Influence Diagnostics

Jillian Fisher , Lang Liu , Krishna Pillutla , Yejin Choi , Zaid Harchaoui This is my paper

classification stat.ML cs.LGmath.STstat.TH

keywords influencediagnosticsstatisticalapproximateboundscomputationaldatapointsfunctions

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Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications. Influence diagnostics are powerful statistical tools to identify influential datapoints or subsets of datapoints. We establish finite-sample statistical bounds, as well as computational complexity bounds, for influence functions and approximate maximum influence perturbations using efficient inverse-Hessian-vector product implementations. We illustrate our results with generalized linear models and large attention based models on synthetic and real data.

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Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics
stat.ML 2026-07 accept novelty 7.0

Under Gaussian design with n ≍ d, the empirical distribution of leave-one-out influences for convex M-estimators converges to the pushforward of a four-dimensional Gaussian through an explicit nonlinear map built from...