{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-unlearning-via-randomized-conditionally","title":"Deep Unlearning via Randomized Conditionally Independent Hessians","arxiv_id":"2204.07655","date":"2022-04-15","proceeding":"CVPR 2022 1","authors":["Ronak Mehta","Sourav Pal","Vikas Singh","Sathya N. Ravi"],"abstract":"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. 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