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Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

An estimator can be accurate and stable on typical data, yet sensitive to a few possible records. Differentially private mechanisms commonly scale their noise to the worst-case influence of a single record, which lets these records dictate the noise even when the remaining data would allow far less. Removing them first would restore the smaller scale, but the removal must itself be private. We i

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#67 most recent of 703 cs.LG papers we have recorded · ↑ newer: Empirical Evaluation of Quantum Kernel Methods for Classification: A C · ↓ older: Adaptive Scenario Generation for KL-DRO Around a Learned Density Model
Cite this page: Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering: the #67 most recent of 703 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/propose-filter-release-scaling-noise-to-typical-sensitivity-via-private-filterin.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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