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We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearning.
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Alastair J Walker · 1977
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Arkadij Semenovich Nemirovskij and David Borisovich Yudin · 1983
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Game-theoretical problems of synthesis of signal generation and reception algorithms
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The mnist database of handwritten digits
Yann LeCun · 1998
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Optimal transport: old and new
Cédric Villani · 2008
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An optimal method for stochastic composite optimization
Guanghui Lan · 2012
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Rényi divergence and kullback-leibler divergence
Tim Van Erven and Peter Harremos · 2014
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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On maximal agreement couplings
Florian Völlering · 2016
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The total variation distance between high-dimensional gaussians
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
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Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2019
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Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Guha Thakurta · 2019
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Mexit: Maximal un-coupling times for stochastic processes
Philip A Ernst, Wilfrid S Kendall, Gareth O Roberts, and Jeffrey S Rosenthal · 2019
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Blake E Woodworth and Nati Srebro · 2016
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Rényi differential privacy
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How to make the gradients small stochastically: Even faster convex and nonconvex sgd
Zeyuan Allen-Zhu · 2018
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Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens van der Maaten · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Approximate data deletion from machine learning models: Algorithms and evaluations
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2020
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Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2020
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Right to be forgotten — Wikipedia, the free encyclopedia
Wikipedia · 2021
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