Deep Unlearning via Randomized Conditionally Independent Hessians

Recent legislation has led to interest in machine unlearning, i. e., removing specific training samples from a predictive model as if they never existed in the training dataset. Unlearning may also be required due to corrupted/adversarial data or simply a user's updated privacy requirement. For...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2022; s. 10412 - 10421
Hlavní autori:	Mehta, Ronak, Pal, Sourav, Singh, Vikas, Ravi, Sathya N.
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2022
Predmet:	accountability Computational modeling Face recognition fairness Law Legislation Predictive models privacy and ethics in vision; Machine learning; Optimization methods; Statistical methods Semantics Training Transparency
ISSN:	1063-6919, 1063-6919
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