2023

Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy

Shaik, Thanveer, Tao, Xiaohui, Xie, Haoran et al.

Understand

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.

While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and fairness.
This paper presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates.
In addition, commonly used metrics and datasets are also presented.

Built on

Edinburgh: Oliver and Boyd, 1963
R. A. Fisher, F. Yates, et al · 1963
Earlier work this paper cites.
R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac, “The multi-purpose incremental learning system aq15 and its testing application to three medical domains,” in Proc. AAAI
1986
Earlier work this paper cites.
J. W. Smith, J. E. Everhart, W. Dickson, W. C. Knowler, and R. S. Johannes, “Using the adap learning algorithm to forecast the onset of diabetes mellitus,” in Proceedings of the annual symposium on computer application in medical care
1988
Earlier work this paper cites.
C. Apté, F. Damerau, and S. M. Weiss, “Automated learning of decision rules for text categorization,” ACM Transactions on Information Systems (TOIS)
1994
Earlier work this paper cites.
T. Joachims, “A probabilistic analysis of the rocchio algorithm with tfidf for text categorization.,” tech. rep., Carnegie-mellon univ pittsburgh pa dept of computer science, 1996
1996
Earlier work this paper cites.
R. Kohavi et al
1996
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
G. Cauwenberghs and T. Poggio, “Incremental and decremental support vector machine learning,” Advances in neural information processing systems
2000
Earlier work this paper cites.
R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Physical Review E
2001
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in 2004 conference on computer vision and pattern recognition workshop
2004
Earlier work this paper cites.
E. Romero, I. Barrio, and L. Belanche, “Incremental and decremental learning for linear support vector machines,” in Artificial Neural Networks–ICANN 2007: 17th Int’l conf., Porto, Portugal, September 9-13, 2007, Proceedings, Part I 17
2007
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition
2009
Earlier work this paper cites.
L. Wasserman and S. Zhou, “A statistical framework for differential privacy,” Journal of the American Statistical Association
2010
Earlier work this paper cites.
A.-r. Mohamed, G. E. Dahl, and G. Hinton, “Acoustic modeling using deep belief networks,” IEEE transactions on audio, speech, and language processing
2011
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS Workshop on Deep Learning and Unsupervised Feature Learning
2011
Earlier work this paper cites.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
2011
Earlier work this paper cites.
L. Wolf, T. Hassner, and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” in CVPR 2011
2011
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine,” in Ambient Assisted Living and Home Care: 4th Int’l Workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings 4
2012
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, J. L. Reyes-Ortiz, et al
2013
Earlier work this paper cites.
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in Int’l conf. on machine learning
2013
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in Advances in Neural Information Processing Systems
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13
2014
Earlier work this paper cites.
C.-H. Tsai, C.-Y. Lin, and C.-J. Lin, “Incremental and decremental training for linear classification,” in Proc. of 20th ACM SIGKDD Int’l conf. on Knowledge discovery and data mining
2014
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE Symposium on Security and Privacy
2015
Earlier work this paper cites.
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,” arXiv preprint arXiv:1506.03365
Original
2015
Earlier work this paper cites.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems
2015
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” ACM Trans. Interact. Intell. Syst
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al
2016
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “Squad: 100,000+ questions for machine comprehension of text,” arXiv preprint arXiv:1606.05250
Original
2016
Earlier work this paper cites.
N. Phan, X. Wu, H. Hu, and D. Dou, “Adaptive laplace mechanism: Differential privacy preservation in deep learning,” in 2017 IEEE Int’l conf. on data mining (ICDM)
2017
Earlier work this paper cites.
A. Madva, “Biased against debiasing: On the role of (institutionally sponsored) self-transformation in the struggle against prejudice,” 2017
2017
Earlier work this paper cites.
X. Yu, T. Liu, X. Wang, and D. Tao, “On compressing deep models by low rank and sparse decomposition,” in Proc. of the IEEE conf. on computer vision and pattern recognition
2017
Earlier work this paper cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” Advances in neural information processing systems
2017
Earlier work this paper cites.
M. R. Bonyadi and Z. Michalewicz, “Particle swarm optimization for single objective continuous space problems: A review,” Evolutionary Computation
2017
Earlier work this paper cites.
Springer International Publishing, 2017
P. Voigt and A. von dem Bussche, The EU General Data Protection Regulation (GDPR) · 2017
Earlier work this paper cites.
H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” arXiv preprint arXiv:1708.07747
Original
2017
Earlier work this paper cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083
Original
2017
Earlier work this paper cites.
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, “On fairness and calibration,” Advances in neural information processing systems
2017
Earlier work this paper cites.
S. Wachter, B. Mittelstadt, and L. Floridi, “Transparent, explainable, and accountable ai for robotics,” Science robotics
2017
Earlier work this paper cites.
J. Lu, T. Issaranon, and D. A. Forsyth, “Feature-guided black-box safety testing of deep neural networks,” in Proc. ICCV
2017
Earlier work this paper cites.
E. F. Villaronga, P. Kieseberg, and T. Li, “Humans forget, machines remember: Artificial intelligence and the right to be forgotten,” Computer Law & Security Review
2018
Earlier work this paper cites.
A. Ashraf, S. Khan, N. Bhagwat, M. Chakravarty, and B. Taati, “Learning to unlearn: Building immunity to dataset bias in medical imaging studies,” arXiv preprint arXiv:1812.01716
Original
2018
Earlier work this paper cites.
M. Riemer, I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, and G. Tesauro, “Learning to learn without forgetting by maximizing transfer and minimizing interference,” arXiv preprint arXiv:1810.11910
Original
2018
Earlier work this paper cites.
F. Musanna and S. Kumar, “A novel fractional order chaos-based image encryption using fisher yates algorithm and 3-d cat map,” Multimedia Tools and Applications
2018
Earlier work this paper cites.
A. Elmahdy and S. Mohajer, “On the fundamental limits of coded data shuffling,” in 2018 IEEE Int’l Symposium on Information Theory (ISIT)
2018
Earlier work this paper cites.
J. Frankle and M. Carbin, “The lottery ticket hypothesis: Finding sparse, trainable neural networks,” arXiv preprint arXiv:1803.03635
Original
2018
Earlier work this paper cites.
A. Ross and F. Doshi-Velez, “Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,” in Proceedings of the AAAI Conference on Artificial Intelligence
2018
Earlier work this paper cites.
S. Mohseni, J. E. Block, and E. D. Ragan, “A human-grounded evaluation benchmark for local explanations of machine learning,” arXiv preprint arXiv:1801.05075
Original
2018
Earlier work this paper cites.
T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,” Future healthcare journal
2019
Earlier work this paper cites.
C. J. Hoofnagle, B. Van Der Sloot, and F. Z. Borgesius, “The european union general data protection regulation: what it is and what it means,” Information & Communications Technology Law
2019
Earlier work this paper cites.
P. Mehta, M. Bukov, C.-H. Wang, A. G. Day, C. Richardson, C. K. Fisher, and D. J. Schwab, “A high-bias, low-variance introduction to machine learning for physicists,” Physics reports
2019
Earlier work this paper cites.
H. He, S. Zha, and H. Wang, “Unlearn dataset bias in natural language inference by fitting the residual,” in Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
2019
Earlier work this paper cites.
S. Murthy, A. A. Bakar, F. A. Rahim, and R. Ramli, “A comparative study of data anonymization techniques,” in 2019 IEEE 5th Intl Conf. on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conf. on High Performance and Smart Computing,(HPSC) and IEEE Intl Conf. on Intelligent Data and Security (IDS)
2019
Earlier work this paper cites.
M. Di Martino, P. Robyns, W. Weyts, P. Quax, W. Lamotte, and K. Andries, “Personal information leakage by abusing the gdpr’right of access’,” USENIX, 2019
2019
Earlier work this paper cites.
D. P. Kingma, M. Welling, et al
2019
Earlier work this paper cites.
Z. He, T. Zhang, and R. B. Lee, “Model inversion attacks against collaborative inference,” in Proceedings of the 35th Annual Computer Security Applications Conference
2019
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2019
Earlier work this paper cites.
C. O. Sakar, S. O. Polat, M. Katircioglu, and Y. Kastro, “Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and lstm recurrent neural networks,” Neural Computing and Applications
2019
Earlier work this paper cites.
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural networks
2019
Earlier work this paper cites.
M. Du, Z. Chen, C. Liu, R. Oak, and D. Song, “Lifelong anomaly detection through unlearning,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
2019
Earlier work this paper cites.
C. N. Fortner, “Decision making within a cancel culture environment,” tech. rep., US Army Command and General Staff College, 2020
2020
Earlier work this paper cites.
J. Globocnik, “The right to be forgotten is taking shape: Cjeu judgments in gc and others (c-136/17) and google v cnil (c-507/17),” GRUR International
2020
Earlier work this paper cites.
A. J. Saquella, “Personal data vulnerability: Constitutional issues with the california consumer privacy act,” Jurimetrics
2020
Earlier work this paper cites.
J. Hinds, E. J. Williams, and A. N. Joinson, ““it wouldn’t happen to me”: Privacy concerns and perspectives following the cambridge analytica scandal,” International Journal of Human-Computer Studies
2020
Earlier work this paper cites.
W. Xu, J. He, and Y. Shu, “Transfer learning and deep domain adaptation,” Advances and applications in deep learning
2020
Earlier work this paper cites.
C. Fu, Y. Zheng, Y. Liu, Q. Xuan, and G. Chen, “NES-TL: Network embedding similarity-based transfer learning,” IEEE Transactions on Network Science and Engineering
2020
Earlier work this paper cites.
K. Bu, Y. He, X. Jing, and J. Han, “Adversarial transfer learning for deep learning based automatic modulation classification,” IEEE Signal Processing Letters
2020
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM
2020
Earlier work this paper cites.
H. Tanaka, D. Kunin, D. L. Yamins, and S. Ganguli, “Pruning neural networks without any data by iteratively conserving synaptic flow,” Advances in neural information processing systems
2020
Earlier work this paper cites.
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec, “Open graph benchmark: Datasets for machine learning on graphs,” Advances in neural information processing systems
2020
Earlier work this paper cites.

Similar

A. Golatkar, A. Achille, and S. Soatto, “Eternal sunshine of the spotless net: Selective forgetting in deep networks,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2020
Cited alongside, same era.
D. M. Sommer, L. Song, S. Wagh, and P. Mittal, “Towards probabilistic verification of machine unlearning,” arXiv preprint arXiv:2003.04247
Original
2020
Cited alongside, same era.
Y.-H. Wu, C.-H. Yuan, and S.-H. Wu, “Adversarial robustness via runtime masking and cleansing,” in Int’l conf. on Machine Learning
2020
Cited alongside, same era.
S. Park and E.-J. Kim, “Exploring linkages between unlearning and human resource development: Revisiting unlearning cases,” Human Resource Development Quarterly
2020
Cited alongside, same era.
J. Brophy, “Exit through the training data: A look into instance-attribution explanations and efficient data deletion in machine learning,” Technical report
2020
Cited alongside, same era.
H. Hwang, G.-H. Kim, S. Hong, and K.-E. Kim, “Variational interaction information maximization for cross-domain disentanglement,” Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
Y. Yin, B. Huang, Y. Wu, and M. Soleymani, “Speaker-invariant adversarial domain adaptation for emotion recognition,” in Proceedings of the 2020 Int’l conf. on Multimodal Interaction
2020
Cited alongside, same era.
A. K. Kar and Y. K. Dwivedi, “Theory building with big data-driven research – moving away from the “what” towards the “why”,” International Journal of Information Management
2020
Cited alongside, same era.
A. Hsu, W. Khoo, N. Goyal, and M. Wainstein, “Next-generation digital ecosystem for climate data mining and knowledge discovery: A review of digital data collection technologies,” Frontiers in Big Data
2020
Cited alongside, same era.
J. L. Lobo, J. Del Ser, A. Bifet, and N. Kasabov, “Spiking neural networks and online learning: An overview and perspectives,” Neural Networks
2020
Cited alongside, same era.
G. A. Kaissis, M. R. Makowski, D. Rückert, and R. F. Braren, “Secure, privacy-preserving and federated machine learning in medical imaging,” Nature Machine Intelligence
2020
Cited alongside, same era.
I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN computer science
2021
Cited alongside, same era.

Then

G. Sun, Y. Cong, J. Dong, Q. Wang, L. Lyu, and J. Liu, “Data poisoning attacks on federated machine learning,” IEEE Internet of Things Journal
2022
Later among the works it cites.
F. A. Yerlikaya and Ş. Bahtiyar, “Data poisoning attacks against machine learning algorithms,” Expert Systems with Applications
2022
Later among the works it cites.
M. Anisetti, C. A. Ardagna, A. Balestrucci, N. Bena, E. Damiani, and C. Y. Yeun, “On the robustness of ensemble-based machine learning against data poisoning,” 2022
2022
Later among the works it cites.
M. Maabreh, O. Darwish, O. Karajeh, and Y. Tashtoush, “On developing deep learning models with particle swarm optimization in the presence of poisoning attacks,” in 2022 Int’l Arab Conference on Information Technology (ACIT)
2022
Later among the works it cites.
A. Thudi, I. Shumailov, F. Boenisch, and N. Papernot, “Bounding membership inference,” arXiv preprint arXiv:2202.12232
Original
2022
Later among the works it cites.
J. Fan, K. Wu, Y. Zhou, Z. Zhao, and S. Huang, “Fast model update for iot traffic anomaly detection with machine unlearning,” 2022
2022
Later among the works it cites.
S. Liu, Z. Lian, S. Gu, and L. Xiao, “Block shuffling learning for deepfake detection,” arXiv preprint arXiv:2202.02819
Original
2022
Later among the works it cites.
M. Jegorova, C. Kaul, C. Mayor, A. Q. O'Neil, A. Weir, R. Murray-Smith, and S. A. Tsaftaris, “Survey: Leakage and privacy at inference time,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2022
Later among the works it cites.
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli, “Zero-shot machine unlearning,” arXiv preprint arXiv:2201.05629
Original
2022
Later among the works it cites.
Y. Li, X. Zheng, C. Chen, and J. Liu, “Making recommender systems forget: Learning and unlearning for erasable recommendation,” arXiv preprint arXiv:2203.11491
Original
2022
Later among the works it cites.
Z. Ma, Y. Liu, X. Liu, J. Liu, J. Ma, and K. Ren, “Learn to forget: Machine unlearning via neuron masking,” IEEE Transactions on Dependable and Secure Computing
2022
Later among the works it cites.
A. Thudi, G. Deza, V. Chandrasekaran, and N. Papernot, “Unrolling sgd: Understanding factors influencing machine unlearning,” in 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)
2022
Later among the works it cites.
A. Setlur, B. Eysenbach, V. Smith, and S. Levine, “Adversarial unlearning: Reducing confidence along adversarial directions,” arXiv preprint arXiv:2206.01367
Original
2022
Later among the works it cites.
T. Baumhauer, P. Schöttle, and M. Zeppelzauer, “Machine unlearning: Linear filtration for logit-based classifiers,” Machine Learning
2022
Later among the works it cites.
Z. Zhou, X. Liu, J. Li, J. Ruan, and M. Fan, “Dynamically selected mixup machine unlearning,” in 2022 IEEE Int’l conf. on Trust, Security and Privacy in Computing and Communications (TrustCom)
2022
Later among the works it cites.
J. Wang, S. Guo, X. Xie, and H. Qi, “Federated unlearning via class-discriminative pruning,” in Proceedings of the ACM Web Conference 2022
2022
Later among the works it cites.
C. Wu, S. Zhu, and P. Mitra, “Federated unlearning with knowledge distillation,” arXiv preprint arXiv:2201.09441
Original
2022
Later among the works it cites.
Y. Yoon, J. Nam, H. Yun, D. Kim, and J. Ok, “Few-shot unlearning by model inversion,” arXiv preprint arXiv:2205.15567
Original
2022
Later among the works it cites.
M. Zhao, M. Li, S.-L. Peng, and J. Li, “A novel deep learning model compression algorithm,” Electronics
2022
Later among the works it cites.
J. Guo, A. Li, and C. Liu, “Backdoor detection in reinforcement learning,” arXiv preprint arXiv:2202.03609
Original
2022
Later among the works it cites.
Y. Yu and J. Liu, “Don’t watch me: A spatio-temporal trojan attack on deep-reinforcement-learning-augment autonomous driving,” arXiv preprint arXiv:2211.14440
Original
2022
Later among the works it cites.
Z. Zhang, M. Tian, C. Li, Y. Huang, and L. Yang, “Poison neural network-based mmwave beam selection and detoxification with machine unlearning,” IEEE Transactions on Communications
2022
Later among the works it cites.
Y. Liu, L. Xu, X. Yuan, C. Wang, and B. Li, “The right to be forgotten in federated learning: An efficient realization with rapid retraining,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications
2022
Later among the works it cites.
R. Pradhan, J. Zhu, B. Glavic, and B. Salimi, “Interpretable data-based explanations for fairness debugging,” in Proceedings of the 2022 Int’l conf. on Management of Data
2022
Later among the works it cites.
A. Thudi, H. Jia, I. Shumailov, and N. Papernot, “On the necessity of auditable algorithmic definitions for machine unlearning,” in 31st USENIX Security Symposium (USENIX Security 22)
2022
Later among the works it cites.
M. C. Vu and L. A. Nguyen, “Mindful unlearning in unprecedented times: Implications for management and organizations,” Management Learning
2022
Later among the works it cites.
Z. Tóth, R. Caruana, T. Gruber, and C. Loebbecke, “The dawn of the AI robots: Towards a new framework of AI robot accountability,” Journal of Business Ethics
2022
Later among the works it cites.
M. M. Bühler, T. Jelinek, and K. Nübel, “Training and preparing tomorrow’s workforce for the fourth industrial revolution,” Education Sciences
2022
Later among the works it cites.
PhD thesis, University of Sheffield, 2022
Z. Zhao, Using Pre-trained Language Models for Toxic Comment Classification · 2022
Later among the works it cites.
M. Goldblum, D. Tsipras, C. Xie, X. Chen, A. Schwarzschild, D. Song, A. Madry, B. Li, and T. Goldstein, “Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2022
Later among the works it cites.
A. K. Tarun, V. S. Chundawat, M. Mandal, and M. Kankanhalli, “Deep regression unlearning,” arXiv preprint arXiv:2210.08196
Original
2022
Later among the works it cites.
E. Chien, C. Pan, and O. Milenkovic, “Certified graph unlearning,” arXiv preprint arXiv:2206.09140
Original
2022
Later among the works it cites.
H. Jia, H. Chen, J. Guan, A. S. Shamsabadi, and N. Papernot, “A zest of lime: Towards architecture-independent model distances,” in Int’l conf. on Learning Representations
2022
Later among the works it cites.
D. Kaur, S. Uslu, K. J. Rittichier, and A. Durresi, “Trustworthy artificial intelligence: a review,” ACM Computing Surveys (CSUR)
2022
Later among the works it cites.
D. Saunders, “Domain adaptation and multi-domain adaptation for neural machine translation: A survey,” Journal of Artificial Intelligence Research
2022
Later among the works it cites.
P. Rana, A. Sowmya, E. Meijering, and Y. Song, “Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification,” Scientific Reports
2022
Later among the works it cites.
Z. Zhang, Y. Zhou, X. Zhao, T. Che, and L. Lyu, “Prompt certified machine unlearning with randomized gradient smoothing and quantization,” in Advances in Neural Information Processing Systems
2022
Later among the works it cites.
P. K. R. Maddikunta, Q.-V. Pham, D. C. Nguyen, T. Huynh-The, O. Aouedi, G. Yenduri, S. Bhattacharya, and T. R. Gadekallu, “Incentive techniques for the internet of things: a survey,” Journal of Network and Computer Applications
2022
Later among the works it cites.
A. Tuladhar, D. Rajashekar, and N. D. Forkert, “Distributed learning in healthcare,” in Trends of Artificial Intelligence and Big Data for E-Health
2022
Later among the works it cites.
S. Mondal and Z. Rehena, “Challenges and limitations of social data analysis approaches,” in Smart Computing and Intelligence
2022
Later among the works it cites.
J. Z. Di, J. Douglas, J. Acharya, G. Kamath, and A. Sekhari, “Hidden poison: Machine unlearning enables camouflaged poisoning attacks,” in NeurIPS ML Safety Workshop
2022
Later among the works it cites.
T. Shaik, X. Tao, Y. Li, C. Dann, J. McDonald, P. Redmond, and L. Galligan, “A review of the trends and challenges in adopting natural language processing methods for education feedback analysis,” IEEE Access
2022
Later among the works it cites.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings
2022
Later among the works it cites.
J. Stacey, Y. Belinkov, and M. Rei, “Supervising model attention with human explanations for robust natural language inference,” in Proceedings of the AAAI Conference on Artificial Intelligence
2022
Later among the works it cites.
I. H. Sarker, “Multi-aspects ai-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview,” Security and Privacy
2022
Later among the works it cites.
C. Chen, F. Sun, M. Zhang, and B. Ding, “Recommendation unlearning,” in Proc. of the ACM Web Conference 2022
2022
Later among the works it cites.
Y. Yao, C. Wang, and H. Li, “Counterfactually evaluating explanations in recommender systems,” arXiv preprint arXiv:2203.01310
Original
2022
Later among the works it cites.
H. Zhang, T. Nakamura, T. Isohara, and K. Sakurai, “A review on machine unlearning,” SN Computer Science
2023
Closest in time.
J. A. S. A. Guimarães, “Preserving personal dignity: the vital role of the right to be forgotten,” Brazilian Journal of Law, Technology and Innovation
2023
Closest in time.
A. K. Tarun, V. S. Chundawat, M. Mandal, and M. Kankanhalli, “Fast yet effective machine unlearning,” 2023
2023
Closest in time.
PhD thesis, Dissertation, Bochum, Ruhr-Universität Bochum, 2022, 2023
T. Schnitzler, Analyzing privacy and end user information exposure in digital communication environments · 2023
Closest in time.
X. Han, T. Baldwin, and T. Cohn, “Everybody needs good neighbours: An unsupervised locality-based method for bias mitigation,” in The Eleventh Int’l conf. on Learning Representations
2023
Closest in time.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al
Original
2023
Closest in time.
A. Koskela and T. Kulkarni, “Practical differentially private hyperparameter tuning with subsampling,” arXiv preprint arXiv:2301.11989
Original
2023
Closest in time.
J. Jia, J. Liu, P. Ram, Y. Yao, G. Liu, Y. Liu, P. Sharma, and S. Liu, “Model sparsification can simplify machine unlearning,” arXiv preprint arXiv:2304.04934
Original
2023
Closest in time.
X. Zhu, G. Li, and W. Hu, “Heterogeneous federated knowledge graph embedding learning and unlearning,” arXiv preprint arXiv:2302.02069
Original
2023
Closest in time.
K. Y. Tan, L. Yueming, Y.-S. Ong, and I. Tsang, “Unfolded self-reconstruction lsh: Towards machine unlearning in approximate nearest neighbour search,” arXiv preprint arXiv:2304.02350
Original
2023
Closest in time.
H. Zheng, X. Li, J. Chen, J. Dong, Y. Zhang, and C. Lin, “One4all: Manipulate one agent to poison the cooperative multi-agent reinforcement learning,” Computers & Security
2023
Closest in time.
J. Guo, Y. Li, X. Chen, H. Guo, L. Sun, and C. Liu, “Scale-up: An efficient black-box input-level backdoor detection via analyzing scaled prediction consistency,” arXiv preprint arXiv:2302.03251
Original
2023
Closest in time.
A. Achille, M. Kearns, C. Klingenberg, and S. Soatto, “Ai model disgorgement: Methods and choices,” arXiv preprint arXiv:2304.03545
Original
2023
Closest in time.
S. Moon, S. Cho, and D. Kim, “Feature unlearning for generative models via implicit feedback,” arXiv preprint arXiv:2303.05699
Original
2023
Closest in time.
S. Sajid, “A methodology to build interpretable machine learning models in organizations,” Master’s thesis, University of Twente, 2023
2023
Closest in time.
V.-D. Păvăloaia and S.-C. Necula, “Artificial intelligence as a disruptive technology—a systematic literature review,” Electronics
2023
Closest in time.
L. Manning, W. Morris, and I. Birchmore, “Organizational unlearning: A risky food safety strategy?,” Comprehensive Reviews in Food Science and Food Safety
2023
Closest in time.
X. Wang, G. Chen, G. Qian, P. Gao, X.-Y. Wei, Y. Wang, Y. Tian, and W. Gao, “Large-scale multi-modal pre-trained models: A comprehensive survey,” arXiv preprint arXiv:2302.10035
Original
2023
Closest in time.
A. Kalinowska, P. M. Pilarski, and T. D. Murphey, “Embodied communication: How robots and people communicate through physical interaction,” Annual Review of Control, Robotics, and Autonomous Systems
2023
Closest in time.
E. Strelcenia and S. Prakoonwit, “A survey on GAN techniques for data augmentation to address the imbalanced data issues in credit card fraud detection,” Machine Learning and Knowledge Extraction
2023
Closest in time.
N. Rathi, I. Chakraborty, A. Kosta, A. Sengupta, A. Ankit, P. Panda, and K. Roy, “Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware,” ACM Computing Surveys
2023
Closest in time.
W. Yuan, H. Yin, F. Wu, S. Zhang, T. He, and H. Wang, “Federated unlearning for on-device recommendation,” in Proceedings of the 16th ACM Int’l conf. on Web Search and Data Mining
2023
Closest in time.
G. Li, L. Shen, Y. Sun, Y. Hu, H. Hu, and D. Tao, “Subspace based federated unlearning,” arXiv preprint arXiv:2302.12448
Original
2023
Closest in time.
PhD thesis, 2023
J. Fan, Machine learning and unlearning for IoT anomaly detection · 2023
Closest in time.
H. Yin, Y. Sun, G. Xu, and E. Kanoulas, “Trustworthy recommendation and search: Introduction to the special issue-part 1,” 2023
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…