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Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models.
Exact calculation of the hessian matrix for the multilayer perceptron, 1992
Bishop, C · 1992
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Multi-digit number recognition from street view imagery using deep convolutional neural networks
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A minimal subset of features using feature selection for handwritten digit recognition
Alsaafin, A. and Elnagar, A · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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On second-order group influence functions for black-box predictions
Basu, S., You, X., and Feizi, S · 2020
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Very deep vaes generalize autoregressive models and can outperform them on images
Child, R · 2020
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Golatkar, A., Achille, A., and Soatto, S · 2020
Cited alongside, same era.
Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L · 2020
Cited alongside, same era.
Gradient surgery for multi-task learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C · 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
Federaser: Enabling efficient client-level data removal from federated learning models
Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J · 2021
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On memorization in probabilistic deep generative models
van den Burg, G. and Williams, C · 2021
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Knowledge removal in sampling-based bayesian inference
Fu, S., He, F., and Tao, D · 2022
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Continual learning and private unlearning
Liu, B., Liu, Q., and Stone, P · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Prompt certified machine unlearning with randomized gradient smoothing and quantization
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Cited alongside, same era.
Mixed-privacy forgetting in deep networks
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., and Soatto, S · 2021
Cited alongside, same era.
Adaptive machine unlearning
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
Cited alongside, same era.
Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A., Achille, A., and Soatto, S
Cited in the paper.
Recon: Reducing conflicting gradients from the root for multi-task learning
Guangyuan, S., Li, Q., Zhang, W., Chen, J., and Wu, X.-M
Cited in the paper.
Conflict-averse gradient descent for multi-task learning
Liu, B., Liu, X., Jin, X., Stone, P., and Liu, Q
Cited in the paper.
Zhang, Z., Zhou, Y., Zhao, X., Che, T., and Lyu, L · 2022
Later among the works it cites.
Feature unlearning for generative models via implicit feedback
Moon, S., Cho, S., and Kim, D · 2023
Closest in time.
Regularizing second-order influences for continual learning
Sun, Z., Mu, Y., and Hua, G · 2023
Closest in time.