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- claim-1-new-analysis-of-influence-functions-and-newton-step-for-convex-erm-replaces-global-strong-convexity-assumptions-with-local-conditions
- claim-2-bounds-for-logistic-regression-with-gaussian-features-capture-the-correct-scaling-up-to-polylogarithmic-factors-yielding-matching-upper-and-lower-bounds
- claim-3-newton-step-shows-markedly-more-accurate-data-attribution-than-influence-functions-explaining-open-discrepancies-between-scalar-causal-scores-and-interpretability
- conclusion
- executive-summary
- 1.23 kB