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arXiv Machine Learning By Ami Tavory, Shripad Gade, Tal Sarig, Noam Touitou, Ido Guy

Exact Unlearning via Quantized Sufficient Statistics

Read the original on arXiv Machine Learning →

The paper introduces Quantized Sufficient Statistics (QSS), a method that separates a frozen global schema from mutable, sum‑decomposable content to enable exact unlearning. QSS offers two variants: QSS‑L, which removes a label while keeping the input, and QSS‑E, which removes both input and label by learning a schema without deletable examples. Experiments on 15 vision, text, and tabular datasets show that QSS‑L achieves deletion latencies up to 483× faster than SISA while maintaining comparable accuracy, and QSS‑E quantifies the accuracy trade‑off when all traces of an input are removed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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