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As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of other samples.
A generalized solution of the orthogonal procrustes problem
Peter H Schönemann · 1966
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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A tutorial on spectral clustering
Ulrike von Luxburg · 2007
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The right to be forgotten
Jeffrey Rosen · 2011
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Spectral rotation versus k-means in spectral clustering
Jin Huang, Feiping Nie, and Heng Huang · 2013
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
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Scalable normalized cut with improved spectral rotation
Xiaojun Chen, Feiping Nie, Joshua Zhexue Huang, and Min Yang · 2017
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Fair clustering through fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A generalized power iteration method for solving quadratic problem on the stiefel manifold
Feiping Nie, Rui Zhang, and Xuelong Li · 2017
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Matching node embeddings for graph similarity
Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Fastgcn: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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The california consumer privacy act: towards a european-style privacy regime in the united states
Stuart L Pardau · 2018
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Backups and the right to be forgotten in the gdpr: An uneasy relationship
Eugenia Politou, Alexandra Michota, Efthimios Alepis, Matthias Pocs, and Constantinos Patsakis · 2018
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Deepinf: Social influence prediction with deep learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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Scalable fair clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner · 2019
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Compositional fairness constraints for graph embeddings
Avishek Joey Bose and William L. Hamilton · 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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Guarantees for spectral clustering with fairness constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Peer-to-peer federated learning on graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar · 2019
Cited alongside, same era.
When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 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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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Deep learning identifies synergistic drug combinations for treating COVID-19
Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay · 2021
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Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, and Charles E. Leiserson · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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On the apparent conflict between individual and group fairness
Reuben Binns · 2020
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Cited alongside, same era.
Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Cited alongside, same era.
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How to find your friendly neighborhood: Graph attention design with self-supervision
Dongkwan Kim and Alice Oh · 2021
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A stochastic alternating balance k-means algorithm for fair clustering
Suyun Liu and Luís Nunes Vicente · 2021
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Privacy compliance: Can technology come to the rescue?
Wenqiang Ruan, Mingxin Xu, Haoyang Jia, Zhenhuan Wu, Lushan Song, and Weili Han · 2021
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Interpreting graph neural networks for NLP with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Learning with selective forgetting
Takashi Shibata, Go Irie, Daiki Ikami, and Yu Mitsuzumi · 2021
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Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, and Raman Arora · 2021
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Large graph clustering with simultaneous spectral embedding and discretization
Zhen Wang, Zhaoqing Li, Rong Wang, Feiping Nie, and Xuelong Li · 2021
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Subgraph matching over graph federation
Ye Yuan, Delong Ma, Zhenyu Wen, Zhiwei Zhang, and Guoren Wang · 2021
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Subgraph federated learning with missing neighbor generation
Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu-Ming Yiu · 2021
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Recommendation unlearning
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding · 2022
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Fedgraph: Federated graph learning with intelligent sampling
Fahao Chen, Peng Li, Toshiaki Miyazaki, and Celimuge Wu · 2022
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Eli Chien, Chao Pan, and Olgica Milenkovic · 2022
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Rethinking graph neural networks for anomaly detection
Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li · 2022
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Unrolling SGD: understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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A federated graph neural network framework for privacy-preserving personalization
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi, Yongfeng Huang, and Xing Xie · 2022
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2022
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