{"type":"ScholarlyArticle","title":"Unlearning from Weakly Supervised Learning","authors":[{"name":"Yi Tang","slug":"yi-tang","given":"Yi","family":"Tang","affiliation":"School of Automation , Southeast University , China"},{"name":"Yi Gao","slug":"yi-gao","given":"Yi","family":"Gao","affiliation":"School of Cyber Science and Engineering , Southeast University , China; Key Laboratory of Computer Network and Information Integration ( Southeast University) , Ministry of Education , China"},{"name":"Yong-gang Luo","slug":"yong-gang-luo","given":"Yong-gang","family":"Luo","affiliation":"AI LAB , Chongqing Changan Automobile Co. Ltd."},{"name":"Ju-Cheng Yang","slug":"ju-cheng-yang","given":"Ju-Cheng","family":"Yang","affiliation":"AI LAB , Chongqing Changan Automobile Co. Ltd."},{"name":"Miao Xu","slug":"miao-xu","given":"Miao","family":"Xu","affiliation":"University of Queensland , Australia"},{"name":"Min-Ling Zhang","slug":"min-ling-zhang","given":"Min-Ling","family":"Zhang","affiliation":"School of Computer Science and Engineering , Southeast University , China; Key Laboratory of Computer Network and Information Integration ( Southeast University) , Ministry of Education , China"}],"abstract":"Machine unlearning provides users with the right to remove their privacy data from a well-trained model. Existing approaches of machine unlearning mainly focus on exploring data removing within supervised learning (SL) tasks. However, weakly supervised learning (WSL) is more applicable to real-world scenarios since collecting WSL data is less laborious than collecting fully supervised data. In this paper, we first propose a machine unlearning approach for WSL by updating the model parameters. Motivated by the uniform distributions of untrained model predictions, we derive a formulated target to force the model's predictions of removed data to be indistinguishable. This encourages the model to forget its ability to recognize features of data slated for unlearning. Moreover, we employ formulated targets to transform the classification unlearning into the convex regression, which can significantly reduce computational cost and avoid extra information storage during the training process. Additionally, we discuss how to design a target to ensure the models' predictions of removed data being indistinguishable in different learning scenarios, e.g., SL or WSL. As the flexibility in formulating targets, the proposed approach effectively deals with the WSL problem while still excels in SL models. Empirical studies show the superiority of the proposed approach.","doi":"10.24963/ijcai.2024/553","url":"https://ijcai.org/proceedings/2024/553","pdf":"https://ijcai.org/proceedings/2024/0553.pdf","markdown":"https://ijcai.org/proceedings/2024/553.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/553","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":553,"pages":{"first":5000,"last":5008},"keywords":["Machine Learning: ML: Other","Machine Learning: ML: Weakly supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Arpit et al., 2017] Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste- Julien. A closer look at memorization in deep networks. In Proceedings of the 34th International Conference on Ma- chine Learning, volume 70, pages 233-242, Sydney, Aus- tralia, 2017.","title":"A closer look at memorization in deep networks","authors":"Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, S Maxinder, Tegan Kanwal, Asja Maharaj, Aaron C Fischer, Yoshua Courville, Simon Bengio, Lacoste-Julien","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baumhauer et al., 2022] Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer. Machine unlearning: linear filtration for logit-based classifiers. Mach. Learn., 111(9):3203-3226, 2022.","title":"Machine unlearning: linear filtration for logit-based classifiers","authors":"Thomas Baumhauer, Pascal Schöttle, Matthias Zeppelzauer","year":"2022","venue":"Mach. Learn","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bourtoule et al., 2021] Lucas Bourtoule, Varun Chan- drasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Pa- pernot. Machine unlearning. In Proceedings of 42nd IEEE Symposium on Security and Privacy, pages 141-159, San Francisco, CA, 2021.","title":"Machine unlearning","authors":"Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot","year":"2021","venue":"Proceedings of 42nd IEEE Symposium on Security and Privacy","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Brophy and Lowd, 2021] Jonathan Brophy and Daniel Lowd. Machine unlearning for random forests. In Pro- ceedings of the 38th International Conference on Machine Learning, volume 139, pages 1092-1104, Virtual Event, 2021.","title":"Machine unlearning for random forests","authors":"Lowd Brophy, Jonathan Brophy, Daniel Lowd","year":"2021","venue":"Proceedings of the 38th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chundawat et al., 2023] Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, and Mohan S. Kankanhalli. Can bad teaching induce forgetting? unlearning in deep net- works using an incompetent teacher. In Proceedings of 37th AAAI Conference on Artificial Intelligence, pages 7210-7217, Washington, DC, 2023.","title":"Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher","authors":"S Vikram, Ayush K Chundawat, Murari Tarun, Mohan S Mandal, Kankanhalli","year":"2023","venue":"Proceedings of 37th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cour et al., 2011] Timothée Cour, Benjamin Sapp, and Ben Taskar. Learning from partial labels. J. Mach. Learn. Res., 12:1501-1536, 2011.","title":"Learning from partial labels","authors":"Timothée Cour, Benjamin Sapp, Ben Taskar","year":"2011","venue":"J. Mach. Learn. Res","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Feng and An, 2019a] Lei Feng and Bo An. Partial label learning by semantic difference maximization. In Sarit Kraus, editor, Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 2294-2300, Macao, China, 2019.","title":"Partial label learning by semantic difference maximization","authors":"An ; Feng, Lei Feng, Bo An","year":"2019","venue":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2019/318"},{"position":8,"raw":"[Feng and An, 2019b] Lei Feng and Bo An. Partial label learning with self-guided retraining. In Proceedings of 33rd AAAI Conference on Artificial Intelligence, pages 3542-3549, Honolulu, HA, 2019.","title":"Partial label learning with self-guided retraining","authors":"An ; Feng, Lei Feng, Bo An","year":"2019","venue":"Proceedings of 33rd AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Feng et al., 2020] Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, and Masashi Sugiyama. Provably consistent partial-label learning. In Advances in Neural Information Processing Systems 33, Virtual Event, 2020.","title":"Provably consistent partial-label learning","authors":"Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gao and Zhang, 2021] Yi Gao and Min-Ling Zhang. Dis- criminative complementary-label learning with weighted loss. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Vir- tual Event, volume 139, pages 3587-3597, Virtual Event, 2021.","title":"Discriminative complementary-label learning with weighted loss","authors":"Zhang ; ] Gao, Yi Gao, Min-Ling Zhang","year":"2021","venue":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gao et al., 2023] Yi Gao, Miao Xu, and Min-Ling Zhang. Unbiased risk estimator to multi-labeled complementary label learning. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, pages 3732- 3740, Macao, China, 2023.","title":"Unbiased risk estimator to multi-labeled complementary label learning","authors":"Yi Gao, Miao Xu, Min-Ling Zhang","year":"2023","venue":"Proceedings of the 32nd International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2023/415"},{"position":12,"raw":"[Ginart et al., 2019] Antonio Ginart, Melody Y. Guan, Gre- gory Valiant, and James Zou. Making AI forget you: Data deletion in machine learning. In Advances in Neural In- formation Processing Systems 32, pages 3513-3526, Van- couver, BC, 2019.","title":"Making AI forget you: Data deletion in machine learning","authors":"Antonio Ginart, Melody Y Guan, Gregory Valiant, James Zou","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Golatkar et al., 2020] Aditya Golatkar, Alessandro Achille, and Stefano Soatto. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 9301-9309, Seattle, WA, 2020.","title":"Eternal sunshine of the spotless net: Selective forgetting in deep networks","authors":"Aditya Golatkar, Alessandro Achille, Stefano Soatto","year":"2020","venue":"Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Graves et al., 2021] Laura Graves, Vineel Nagisetty, and Vijay Ganesh. Amnesiac machine learning. In Proceed- ings of 35th AAAI Conference on Artificial Intelligence, pages 11516-11524, Virtual Event, 2021.","title":"Amnesiac machine learning","authors":"Laura Graves, Vineel Nagisetty, Vijay Ganesh","year":"2021","venue":"Proceedings of 35th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guo et al., 2020] Chuan Guo, Tom Goldstein, Awni Y. Han- nun, and Laurens van der Maaten. Certified data re- moval from machine learning models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119, pages 3832-3842, Virtual Event, 2020.","title":"Certified data removal from machine learning models","authors":"Chuan Guo, Tom Goldstein, Awni Y Hannun, Laurens Van Der Maaten","year":"2020","venue":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Huang et al., 2021] Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang. Unlearnable examples: Making personal data unexploitable. In Proceedings of 9th International Conference on Learning Representations, Virtual Event, 2021.","title":"Unlearnable examples: Making personal data unexploitable","authors":"Huang","year":"2021","venue":"Proceedings of 9th International Conference on Learning Representations, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Izzo et al., 2021] Zachary Izzo, Mary Anne Smart, Kama- lika Chaudhuri, and James Zou. Approximate data dele- tion from machine learning models. In Proceedings of 24th International Conference on Artificial Intelligence and Statistics, volume 130, pages 2008-2016, Virtual Event, 2021.","title":"Approximate data deletion from machine learning models","authors":"Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou","year":"2021","venue":"Proceedings of 24th International Conference on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Khan and Swaroop, 2021] Mohammad Emtiyaz Khan and Siddharth Swaroop. Knowledge-adaptation priors. In Advances in Neural Information Processing Systems 34, pages 19757-19770, Virtual Event, 2021.","title":"Mohammad Emtiyaz Khan and Siddharth Swaroop. Knowledge-adaptation priors","authors":"Swaroop Khan","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural In- formation Processing Systems 25, pages 1106-1114, Lake Tahoe, NV, 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":"2012","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Mantelero, 2013] Alessandro Mantelero. The eu proposal for a general data protection regulation and the roots of the 'right to be forgotten'. Computer Law & Security Review, 29(3):229-235, 2013.","title":"The eu proposal for a general data protection regulation and the roots of the 'right to be forgotten","authors":"Alessandro Mantelero","year":"2013","venue":"Computer Law & Security Review","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Nguyen et al., 2022] Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen. A survey of machine unlearning. CoRR, abs/2209.02299, 2022.","title":"A survey of machine unlearning","authors":"Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet, Hung Nguyen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Noh et al., 2015] Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. Learning deconvolution network for se- mantic segmentation. In Proceedings of 2015 IEEE In- ternational Conference on Computer Vision, pages 1520- 1528, Santiago, Chile, 2015.","title":"Learning deconvolution network for semantic segmentation","authors":"Noh","year":"2015","venue":"Proceedings of 2015 IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Patrini et al., 2017] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of 2017 IEEE Confer- ence on Computer Vision and Pattern Recognition, pages 2233-2241, Honolulu, HI, 2017.","title":"Making deep neural networks robust to label noise: A loss correction approach","authors":"Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu","year":"2017","venue":"Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Proserpio et al., 2014] Davide Proserpio, Sharon Goldberg, and Frank McSherry. Calibrating data to sensitivity in pri- vate data analysis. Proc. VLDB Endow., 7(8):637-648, 2014.","title":"Calibrating data to sensitivity in private data analysis","authors":"Davide Proserpio, Sharon Goldberg, Frank Mcsherry","year":"2014","venue":"Proc. VLDB Endow","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sekhari et al., 2021a] Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh. Remem- ber what you want to forget: Algorithms for machine un- learning. In Advances in Neural Information Processing Systems 34, pages 18075-18086, Virtual Event, 2021.","title":"Remember what you want to forget: Algorithms for machine unlearning","authors":"Sekhari","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sekhari et al., 2021b] Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh. Remem- ber what you want to forget: Algorithms for machine un- learning. In Advances in Neural Information Processing Systems 34, pages 18075-18086, Virtual Event, 2021.","title":"Remember what you want to forget: Algorithms for machine unlearning","authors":"Sekhari","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Shen et al., 2024a] Shaofei Shen, Chenhao Zhang, Alina Bialkowski, Weitong Chen, and Miao Xu. Camu: Dis- entangling causal effects in deep model unlearning. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), Virtual Event, 2024.","title":"Camu: Disentangling causal effects in deep model unlearning","authors":"Shen","year":"2024","venue":"Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), Virtual Event","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Shen et al., 2024b] Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Tony Chen, and Miao Xu. Label-agnostic forgetting: A supervision-free un- learning in deep models. In The Twelfth International Con- ference on Learning Representations, Virtual Event, 2024.","title":"Label-agnostic forgetting: A supervision-free unlearning in deep models","authors":"Shen","year":"2024","venue":"The Twelfth International Conference on Learning Representations, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Shi et al., 2023] Yu Shi, Ning Xu, Hua Yuan, and Xin Geng. Unreliable partial label learning with recursive separation. In Proceedings of the 32nd International Joint Confer- ence on Artificial Intelligence, pages 4208-4216, Macao, China, 2023.","title":"Unreliable partial label learning with recursive separation","authors":"Yu Shi, Ning Xu, Hua Yuan, Xin Geng","year":"2023","venue":"Proceedings of the 32nd International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2023/468"},{"position":30,"raw":"[Shintre et al., 2019] Saurabh Shintre, Kevin A. Roundy, and Jasjeet Dhaliwal. Making machine learning forget. In An- nual Privacy Forum, volume 11498, pages 72-83, Rome, Italy, 2019.","title":"Making machine learning forget","authors":"Saurabh Shintre, Kevin A Roundy, Jasjeet Dhaliwal","year":"2019","venue":"Annual Privacy Forum","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shokri et al., 2017] Reza Shokri, Marco Stronati, Con- gzheng Song, and Vitaly Shmatikov. Membership infer- ence attacks against machine learning models. In Proceed- ings of 2017 IEEE Symposium on Security and Privacy, pages 3-18, San Jose, CA, 2017.","title":"Membership inference attacks against machine learning models","authors":"Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov","year":"2017","venue":"Proceedings of 2017 IEEE Symposium on Security and Privacy","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[State of California Department of Justice, 2023] State of California Department of Justice. California consumer privacy act. https://oag.ca.gov/privacy/ccpa, 2023.","title":"California consumer privacy","authors":null,"year":"2023","venue":null,"doi":null,"url":"https://oag.ca.gov/privacy/ccpa,2023","internal_url":null},{"position":33,"raw":"[Tang et al., 2024] Wei Tang, Weijia Zhang, and Min-Ling Zhang. Multi-instance partial-label learning: Towards ex- ploiting dual inexact supervision. Science China Informa- tion Sciences, 67(3):1-14, 2024.","title":"Multi-instance partial-label learning: Towards exploiting dual inexact supervision","authors":"Wei Tang, Weijia Zhang, Min-Ling Zhang","year":"2024","venue":"Science China Information Sciences","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Thudi et al., 2022] Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. Unrolling SGD: understanding factors influencing machine unlearning. In Proceedings of 7th IEEE European Symposium on Secu- rity and Privacy, pages 303-319, Genoa, Italy, 2022.","title":"Unrolling SGD: understanding factors influencing machine unlearning","authors":"Thudi","year":"2022","venue":"Proceedings of 7th IEEE European Symposium on Security and Privacy","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wei et al., 2022] Yi Wei, Mei Xue, Xin Liu, and Pengxiang Xu. Data fusing and joint training for learning with noisy labels. Frontiers of Computer Science, 16(6):166338, 2022.","title":"Data fusing and joint training for learning with noisy labels","authors":"Yi Wei, Mei Xue, Xin Liu, Pengxiang Xu","year":"2022","venue":"Frontiers of Computer Science","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wen et al., 2021] Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, and Zhouchen Lin. Lever- aged weighted loss for partial label learning. In Pro- ceedings of the 38th International Conference on Machine Learning, volume 139, pages 11091-11100, Virtual Event, 2021.","title":"Leveraged weighted loss for partial label learning","authors":"Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin","year":"2021","venue":"Proceedings of the 38th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2022] Ga Wu, Masoud Hashemi, and Christo- pher Srinivasa. PUMA: performance unchanged model augmentation for training data removal. In Proceedings of 36th AAAI Conference on Artificial Intelligence, pages 8675-8682, Virtual Event, 2022.","title":"PUMA: performance unchanged model augmentation for training data removal","authors":"Ga Wu, Masoud Hashemi, Christopher Srinivasa","year":"2022","venue":"Proceedings of 36th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xu et al., 2024] Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu. Machine unlearning: A survey. ACM Comput. Surv., 56(1):9:1-9:36, 2024.","title":"Machine unlearning: A survey","authors":"Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S Yu","year":"2024","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yan et al., 2022] Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin. ARCANE: an efficient architecture for exact machine unlearning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, pages 4006-4013, Vienna, Austria, 2022.","title":"ARCANE: an efficient architecture for exact machine unlearning","authors":"Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, Xiaodong Lin","year":"2022","venue":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2022/556"},{"position":40,"raw":"[Zhang and Sabuncu, 2018] Zhilu Zhang and Mert R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Advances in Neural Information Processing Systems 31, pages 8792-8802, Montréal, Canada, 2018.","title":"Generalized cross entropy loss for training deep neural networks with noisy labels","authors":"Sabuncu ; ] Zhilu Zhang, Mert R Zhang, Sabuncu","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2017] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understand- ing deep learning requires rethinking generalization. In Proceedings of 5th International Conference on Learning Representations, Toulon, France, 2017.","title":"Understanding deep learning requires rethinking generalization","authors":"Zhang","year":"2017","venue":"Proceedings of 5th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhou and Gu, 2018] Yu Zhou and Hong Gu. Geometric mean metric learning for partial label data. Neurocom- puting, 275:394-402, 2018.","title":"Geometric mean metric learning for partial label data","authors":"Yu Zhou, Hong Gu","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou, 2018] Zhi-Hua Zhou. A brief introduction to weakly supervised learning. National science review, 5(1):44-53, 2018.","title":"A brief introduction to weakly supervised learning","authors":"Zhi-Hua Zhou","year":"2018","venue":"National science review","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}