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Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners.
W. Ruan, M. Xu, W. Fang, L. Wang, L. Wang, and W. Han, “Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy,” in Proc. of IEEE S & P , 2023, pp. 1926–1943
1943
Earlier work this paper cites.
F. J. Massey Jr, “The kolmogorov-smirnov test for goodness of fit,” Journal of the American statistical Association , vol. 46, no. 253, pp. 68–78, 1951
1951
Earlier work this paper cites.
C. Feng, N. Xu, W. Wen, P. Venkitasubramaniam, and C. Ding, “Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering,” in Proc. of IEEE S & P , 2023, pp. 1944–1960
1960
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E. Even-dar, S. M. Kakade, and Y. Mansour, “Experts in a Markov Decision Process,” in Proc. of NIPS , 2004
2004
Earlier work this paper cites.
C. Dwork, “Differential Privacy,” in Proc. of International Conference on Automata, Languages and Programming , 2006, pp. 1–12
2006
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing Atari with Deep Reinforcement Learning,” in Proc. of NIPS Deep Learning Workshop , 2013
2013
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Y. Cao and J. Yang, “Towards Making Systems Forget with Machine Unlearning,” in Proc. of IEEE S & P , 2015, pp. 463–480
2015
Earlier work this paper cites.
V. Mnih and et al., “Human-level control through deep reinforcement learning,” Nature , vol. 518, p. 529–533, 2015
2015
Earlier work this paper cites.
GDPR, “General Data Protection Regulation,” https://gdpr-info.eu , 2016
2016
Earlier work this paper cites.
Kaggle, “MovieLens 20M Dataset,” 2016. [Online]. Available: https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset
2016
Earlier work this paper cites.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” in Proc. of ICLR , 2016, pp. 1–14
2016
Earlier work this paper cites.
J. Kirkpatrick and et al., “Overcoming catastrophic forgetting in neural networks,” PNAS , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks Against Machine Learning Models,” in Proc. of IEEE S & P , 2017, pp. 3–18
2017
Earlier work this paper cites.
S. Tosatto, M. Pirotta, C. D’Eramo, and M. Restelli, “Boosted Fitted Q-Iteration,” in Proc. of ICML , 2017
2017
Earlier work this paper cites.
K. Lee, S. Kim, J. Choi, and S. Lee, “Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling,” in Proc. of ICML , 2018, pp. 2937–2946
2018
Earlier work this paper cites.
P. Mirowski, M. K. Grimes, M. Malinowski, K. M. Hermann, K. Anderson, D. Teplyashin, K. Simonyan, K. Kavukcuoglu, A. Zisserman, and R. Hadsell, “Learning to Navigate in Cities Without a Map,” in Proc. of NeurIPS , 2018
2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction . MIT Press, 2018
2018
Cited alongside, same era.
M. Chen, A. Beutel, P. Covington, S. Jain, F. Belletti, and E. Chi, “Top-K Off-Policy Correction for a REINFORCE Recommender System,” in Proc. of WSDM , 2019
2019
Cited alongside, same era.
A. A. Ginart, M. Y. Guan, G. Valiant, and J. Zou, “Making AI Forget You: Data Deletion in Machine Learning,” in Proc. of NIPS , 2019
2019
Cited alongside, same era.
L. Harries, S. Lee, J. Rzepecki, K. Hofmann, and S. Devlin, “MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning,” in Proc. of IEEE Conference on Games (CoG) , 2019
X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, and Q. Miao, “Deep Reinforcement Learning: A Survey,” IEEE Transactions on Neural Networks and Learning Systems , p. DOI: 10.1109/TNNLS.2022.3207346, 2022
2022
Later among the works it cites.
H. Xu, X. Qu, and Z. Rabinovich, “Spiking Pitch Black: Poisoning an Unknown Environment to Attack Unknown Reinforcement Learning,” in Proc. of AAMAS , 2022, pp. 1409–1417
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 Proc. of NIPS , 2022
2022
Later among the works it cites.
Artari, “https://paperswithcode.com/task/atari-games,” 2023
2023
Closest in time.
X. Chen, W. Guo, G. Tao, X. Zhang, and D. Song, “BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning,” in Proc. of NeurIPS , 2023, pp. 1–13
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2019
Cited alongside, same era.
X. Pan, W. Wang, X. Zhang, B. Li, J. Yi, and D. Song, “How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement Learning,” in Proc. of AAMAS , 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
C. Guo, T. Goldstein, A. Hannun, and L. van der Maaten, “Certified Data Removal from Machine Learning Models,” in Proc. of ICML , 2020
2020
Cited alongside, same era.
G. Vietri, B. Balle, A. Krishnamurthy, and S. Wu, “Private Reinforcement Learning with PAC and Regret Guarantees,” in Proc. of ICML , 2020
2020
Cited alongside, same era.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine Unlearning,” in Proc. of IEEE S & P , 2021, pp. 141–159
2021
Cited alongside, same era.
E. Garcelon, V. Perchet, C. Pike-Burke, and M. Pirotta, “Local Differential Privacy for Regret Minimization in Reinforcement Learning,” in Proc. of NIPS , 2021
2021
Cited alongside, same era.
V. Gupta, C. Jung, and S. Neel, “Adaptive Machine Unlearning,” in Proc. of NIPS , 2021
2021
Cited alongside, same era.
2023
Closest in time.
Gym, “https://gymnasium.farama.org,” 2023
2023
Closest in time.
M. Kurmanji, P. Triantafillou, J. Hayes, and E. Triantafillou, “Towards Unbounded Machine Unlearning,” in Proc. of NeurIPS , 2023
2023
Closest in time.
G. Tang, J. Pan, H. Wang, and J. Basilico, “Reward Innovation for Long-term Member Satisfaction,” in Proc. of RecSys , 2023
2023
Closest in time.
A. Thudi, H. Jia, I. Shumailov, and N. Papernot, “On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning,” in Proc. of USENIX Security , 2023
2023
Closest in time.
VirtualHome, “http://virtual-home.org,” 2023
2023
Closest in time.
A. Warnecke, L. Pirch, C. Wressnegger, and K. Rieck, “Machine Unlearning of Features and Labels,” in Proc. of NDSS , 2023
2023
Closest in time.
H. Xu, T. Zhu, L. Zhang, W. Zhou, and P. S. Yu, “Machine Unlearning: A Survey,” ACM Computing Surveys , 2023
2023
Closest in time.
J. Yu, W. Guo, Q. Qin, G. Wang, T. Wang, and X. Xing, “AIRS: Explanation for Deep Reinforcement Learning based Security Applications,” in Proc. of USENIX Security , 2023
2023
Closest in time.
H. Hu, S. Wang, J. Chang, H. Zhong, R. Sun, S. Hao, H. Zhu, and M. Xue, “A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services,” NDSS , 2024
2024
Closest in time.
2024
Closest in time.
C. Zhao, W. Qian, Y. Li, W. Li, and M. Huai, “Rethinking Adversarial Robustness in the Context of the Right to be Forgotten,” in Proc. of ICML , 2024
2024
Closest in time.