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With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models.
Topic-sensitive pagerank
Haveliwala, T. H · 2002
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Har: hub, authority and relevance scores in multi-relational data for query search
Li, X., Ng, M. K., and Ye, Y · 2012
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Bpr: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L · 2012
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S · 2017
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The california consumer privacy act: Towards a european-style privacy regime in the united states
Pardau, S. L · 2018
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General data protection regulation, 2018
Union, E · 2018
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Estimating node importance in knowledge graphs using graph neural networks
Park, N., Kan, A., Dong, X. L., Zhao, T., and Faloutsos, C · 2019
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Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems
Rastegarpanah, B., Gummadi, K. P., and Crovella, M · 2019
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Neural graph collaborative filtering
Wang, X., He, X., Wang, M., Feng, F., and Chua, T.-S · 2019
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Efficient neural matrix factorization without sampling for recommendation
Chen, C., Zhang, M., Zhang, Y., Liu, Y., and Ma, S · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M · 2020
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Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
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Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
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Adaptive machine unlearning
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
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User-oriented fairness in recommendation
Li, Y., Chen, H., Fu, Z., Ge, Y., and Zhang, Y · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Sekhari, A., Acharya, J., Kamath, G., and Suresh, A. T · 2021
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Recommendation unlearning
Chen, C., Sun, F., Zhang, M., and Ding, B · 2022
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Unlearning protected user attributes in recommendations with adversarial training
Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., and Schedl, M · 2022
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Backdoor defense with machine unlearning
Liu, Y., Fan, M., Chen, C., Liu, X., Ma, Z., Wang, L., and Ma, J · 2022
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Learn to forget: Machine unlearning via neuron masking
Ma, Z., Liu, Y., Liu, X., Liu, J., Ma, J., and Ren, K · 2022
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Who’s harry potter? approximate unlearning in llms
Eldan, R. and Russinovich, M · 2023
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California senate bill 362, 2023
Information, C. L · 2023
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A survey on the fairness of recommender systems
Wang, Y., Ma, W., Zhang, M., Liu, Y., and Ma, S · 2023
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Sequence unlearning for sequential recommender systems
Ye, S. and Lu, J · 2023
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Recommendation unlearning via influence function
Zhang, Y., Hu, Z., Bai, Y., Feng, F., Wu, J., Wang, Q., and He, X · 2023
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Challenging forgets: Unveiling the worst-case forget sets in machine unlearning
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Hard to forget: Poisoning attacks on certified machine unlearning
Marchant, N. G., Rubinstein, B. I., and Alfeld, S · 2022
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Deep unlearning via randomized conditionally independent hessians
Mehta, R., Pal, S., Singh, V., and Ravi, S. N · 2022
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Fair infinitesimal jackknife: Mitigating the influence of biased training data points without refitting
Sattigeri, P., Ghosh, S., Padhi, I., Dognin, P., and Varshney, K. R · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning
Thudi, A., Jia, H., Shumailov, I., and Papernot, N · 2022
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Federated unlearning via class-discriminative pruning
Wang, J., Guo, S., Xie, X., and Qi, H · 2022
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Puma: Performance unchanged model augmentation for training data removal
Wu, G., Hashemi, M., and Srinivasa, C · 2022
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Fan, C., Liu, J., Hero, A., and Liu, S · 2024
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Fast machine unlearning without retraining through selective synaptic dampening
Foster, J., Schoepf, S., and Brintrup, A · 2024
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Exact and efficient unlearning for large language model-based recommendation
Hu, Z., Zhang, Y., Xiao, M., Wang, W., Feng, F., and He, X · 2024
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Rwku: Benchmarking real-world knowledge unlearning for large language models
Jin, Z., Cao, P., Wang, C., He, Z., Yuan, H., Li, J., Chen, Y., Liu, K., and Zhao, J · 2024
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Towards unbounded machine unlearning
Kurmanji, M., Triantafillou, P., Hayes, J., and Triantafillou, E · 2024
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Tofu: A task of fictitious unlearning for llms
Maini, P., Feng, Z., Schwarzschild, A., Lipton, Z. C., and Kolter, J. Z · 2024
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Fair machine unlearning: Data removal while mitigating disparities
Oesterling, A., Ma, J., Calmon, F., and Lakkaraju, H · 2024
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Towards efficient and effective unlearning of large language models for recommendation
Wang, H., Lin, J., Chen, B., Yang, Y., Tang, R., Zhang, W., and Yu, Y · 2024
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On the effectiveness of unlearning in session-based recommendation
Xin, X., Yang, L., Zhao, Z., Ren, P., Chen, Z., Ma, J., and Ren, Z · 2024
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Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models
Zhang, Y., Zhang, Y., Yao, Y., Jia, J., Liu, J., Liu, X., and Liu, S · 2024
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