New analysis without global strong convexity yields tight scaling laws: NS error ~Θ(kd/n²) and NS-IF difference ~Θ((k+d)√(kd)/n²) for well-behaved logistic regressions.
Rescaled influence functions: Accurate data attribution in high dimension.arXiv preprint arXiv:2506.06656,
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A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
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On the Accuracy of Newton Step and Influence Function Data Attributions
New analysis without global strong convexity yields tight scaling laws: NS error ~Θ(kd/n²) and NS-IF difference ~Θ((k+d)√(kd)/n²) for well-behaved logistic regressions.
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On the Fragility of Data Attribution When Learning Is Distributed
A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.