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Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet.
Scale-sensitive dimensions, uniform convergence, and learnability
Noga Alon, Shai Ben-David, Nicolo Cesa-Bianchi, and David Haussler · 1997
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Identifying mislabeled training data
Carla E Brodley and Mark A Friedl · 1999
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Differential privacy
Cynthia Dwork · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Foundations of machine learning
Robert E Schapire and Yoav Freund · 2012
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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California consumer privacy act
California State Leglisature · 2018
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Eu general data protection regulation
Council of European Union · 2018
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Proper ResNet implementation for CIFAR10/CIFAR100 in PyTorch
Yerlan Idelbayev · 2018
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Personal information protection and electronic documents act
Parliament of Canada · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Data validation for machine learning
Eric Breck, Marty Zinkevich, Neoklis Polyzotis, Steven Whang, and Sudip Roy · 2019
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Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2019
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Making AI forget you: Data deletion in machine learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant, and James Zou · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
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Poisoning attack in federated learning using generative adversarial nets
Jiale Zhang, Junjun Chen, Di Wu, Bing Chen, and Shui Yu · 2019
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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Metapoison: Practical general-purpose clean-label data poisoning
W Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein · 2020
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Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2020
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New insights and perspectives on the natural gradient method
James Martens · 2020
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Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, and Kilian Q Weinberger · 2020
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"amnesia" - machine learning models that can forget user data very fast
Sebastian Schelter · 2020
Physical backdoor attacks to lane detection systems in autonomous driving
Xingshuo Han, Guowen Xu, Yuan Zhou, Xuehuan Yang, Jiwei Li, and Tianwei Zhang · 2022
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Fairness-aware pac learning from corrupted data
Nikola H Konstantinov and Christoph Lampert · 2022
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Backdoor defense with machine unlearning
Yang Liu, Mingyuan Fan, Cen Chen, Ximeng Liu, Zhuo Ma, Li Wang, and Jianfeng Ma · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
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A survey of machine unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
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How unfair is private learning?
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan B. Davidson · 2020
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Amartya Sanyal, Yaxi Hu, and Fanny Yang · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Athena: Probabilistic Verification of Machine Unlearning
David M. Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal · 2022
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Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee · 2022
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A comprehensive survey on poisoning attacks and countermeasures in machine learning
Zhiyi Tian, Lei Cui, Jie Liang, and Shui Yu · 2022
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Poisoning web-scale training datasets is practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, and Florian Tramèr · 2023
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2023
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Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
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Towards adversarial evaluations for inexact machine unlearning
Shashwat Goel, Ameya Prabhu, Amartya Sanyal, Ser-Nam Lim, Philip Torr, and Ponnurangam Kumaraguru · 2023
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Algorithmic collective action in machine learning
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic · 2023
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Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou · 2023
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Random relabeling for efficient machine unlearning
Junde Li and Swaroop Ghosh · 2023
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A law of adversarial risk, interpolation, and label noise
Daniel Paleka and Amartya Sanyal · 2023
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Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples
Shaokui Wei, Mingda Zhang, Hongyuan Zha, and Baoyuan Wu · 2023
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Few-shot unlearning by model inversion, 2023
Youngsik Yoon, Jinhwan Nam, Hyojeong Yun, Jaeho Lee, Dongwoo Kim, and Jungseul Ok · 2023
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Rethinking label poisoning for GNNs: Pitfalls and attacks
Vijay Lingam, Mohammad Sadegh Akhondzadeh, and Aleksandar Bojchevski · 2024
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