Fetching the paper…
Reading the bibliography…
With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model.
Mathematical methods of organizing and planning production
Kantorovich, L. V · 1960
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Bishop, C. M. and Nasrabadi, N. M · 2006
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Earlier work this paper cites.
Dex: Deep expectation of apparent age from a single image
Rothe, R., Timofte, R., and Van Gool, L · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Temporal regularized matrix factorization for high-dimensional time series prediction
Yu, H.-F., Rao, N., and Dhillon, I. S · 2016
Earlier work this paper cites.
Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., and Specia, L · 2017
Earlier work this paper cites.
Deletion-robust submodular maximization: Data summarization with “the right to be forgotten”
Mirzasoleiman, B., Karbasi, A., and Krause, A · 2017
Earlier work this paper cites.
Agedb: the first manually collected, in-the-wild age database
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., and Zafeiriou, S · 2017
Earlier work this paper cites.
On wasserstein two-sample testing and related families of nonparametric tests
Ramdas, A., García Trillos, N., and Cuturi, M · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A · 2017
Earlier work this paper cites.
Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M. Y., Valiant, G., and Zou, J · 2019
Earlier work this paper cites.
Learning not to learn: Training deep neural networks with biased data
Kim, B., Kim, H., Kim, K., Kim, S., and Kim, J · 2019
Earlier work this paper cites.
Zero-shot knowledge transfer via adversarial belief matching
Micaelli, P. and Storkey, A. J · 2019
Cited alongside, same era.
An introduction to the california consumer privacy act (ccpa)
Goldman, E · 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.
Variational bayesian unlearning
Nguyen, Q. P., Low, B. K. H., and Jaillet, P · 2020
Cited alongside, same era.
Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
Cited alongside, same era.
Machine unlearning for random forests
Brophy, J. and Lowd, D · 2021
Cited alongside, same era.
Machine unlearning via algorithmic stability
Ullah, E., Mai, T., Rao, A., Rossi, R. A., and Arora, R · 2021
Later among the works it cites.
Machine unlearning of features and labels
Warnecke, A., Pirch, L., Wressnegger, C., and Rieck, K · 2021
Later among the works it cites.
Skin deep unlearning: Artefact and instrument debiasing in the context of melanoma classification
Bevan, P. and Atapour-Abarghouei, A · 2022
Closest in time.
The privacy onion effect: Memorization is relative
Carlini, N., Jagielski, M., Papernot, N., Terzis, A., Tramer, F., and Zhang, C · 2022
Closest in time.
Recommendation unlearning
Chen, C., Sun, F., Zhang, M., and Ding, B · 2022
Closest in time.
Graph unlearning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y · 2021
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.
Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
Cited alongside, same era.
Approximate data deletion from machine learning models
Izzo, Z., Smart, M. A., Chaudhuri, K., and Zou, J · 2021
Cited alongside, same era.
Online forgetting process for linear regression models
Li, Y., Wang, C.-H., and Cheng, G · 2021
Cited alongside, same era.
Temporal fusion transformers for interpretable multi-horizon time series forecasting
Lim, B., Arık, S. Ö., Loeff, N., and Pfister, T · 2021
Cited alongside, same era.
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y · 2022
Closest in time.
The right to be forgotten in federated learning: An efficient realization with rapid retraining
Liu, Y., Xu, L., Yuan, X., Wang, C., and Li, B · 2022
Closest in time.
Certifiable unlearning pipelines for logistic regression: An experimental study
Mahadevan, A. and Mathioudakis, M · 2022
Closest in time.
Hard to forget: Poisoning attacks on certified machine unlearning
Marchant, N. G., Rubinstein, B. I., and Alfeld, S · 2022
Closest in time.
Deep unlearning via randomized conditionally independent hessians
Mehta, R., Pal, S., Singh, V., and Ravi, S. N · 2022
Closest in time.
A survey of machine unlearning
Nguyen, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W.-C., Yin, H., and Nguyen, Q. V. H · 2022
Closest in time.
Federated unlearning via class-discriminative pruning
Wang, J., Guo, S., Xie, X., and Qi, H · 2022
Closest in time.
Federated unlearning with knowledge distillation
Wu, C., Zhu, S., and Mitra, P · 2022
Closest in time.
Learning with recoverable forgetting
Ye, J., Yifang, F., Song, J., Yang, X., Liu, S., Jin, X., Song, M., and Wang, X · 2022
Closest in time.
Fast yet effective machine unlearning
Tarun, A. K., Chundawat, V. S., Mandal, M., and Kankanhalli, M · 2023
Closest in time.