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This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased.
A unifying view of sparse approximate Gaussian process regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena
J. Chen, B. K. H. Low, C. K.-Y. Tan, A. Oran, P. Jaillet, J. M. Dolan, and G. S. Sukhatme · 2012
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Parallel Gaussian process regression with low-rank covariance matrix approximations
J. Chen, N. Cao, B. K. H. Low, R. Ouyang, C. K.-Y. Tan, and P. Jaillet · 2013
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Gaussian process-based decentralized data fusion and active sensing for mobility-on-demand system
J. Chen, B. K. H. Low, and C. K.-Y. Tan · 2013
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Gaussian processes for big data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
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The EU proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
A. Mantelero · 2013
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Variational inference in sparse Gaussian process regression and latent variable models–a gentle tutorial
Y. Gal and M. van der Wilk · 2014
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Generalized online sparse Gaussian processes with application to persistent mobile robot localization
B. K. H. Low, N. Xu, J. Chen, K. K. Lim, and E. B. Özgül · 2014
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GP-Localize: Persistent mobile robot localization using online sparse Gaussian process observation model
N. Xu, B. K. H. Low, J. Chen, K. K. Lim, and E. B. Özgül · 2014
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Towards making systems forget with machine unlearning
Y. Cao and J. Yang · 2015
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Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems
J. Chen, B. K. H. Low, P. Jaillet, and Y. Yao · 2015
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A unifying framework of anytime sparse Gaussian process regression models with stochastic variational inference for big data
T. N. Hoang, Q. M. Hoang, and B. K. H. Low · 2015
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Parallel Gaussian process regression for big data: Low-rank representation meets Markov approximation
B. K. H. Low, J. Yu, J. Chen, and P. Jaillet · 2015
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Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
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A distributed variational inference framework for unifying parallel sparse Gaussian process regression models
T. N. Hoang, Q. M. Hoang, and B. K. H. Low · 2016
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L. Bourtoule, V. Chandrasekaran, C. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2019
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Lifelong anomaly detection through unlearning
M. Du, Z. Chen, C. Liu, R. Oak, and D. Song · 2019
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Making AI forget you: Data deletion in machine learning
A. Ginart, M. Guan, G. Valiant, and J. Y. Zou · 2019
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Certified data removal from machine learning models
C. Guo, T. Goldstein, A. Hannun, and L. van der Maaten · 2019
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Collective model fusion for multiple black-box experts
Q. M. Hoang, T. N. Hoang, B. K. H. Low, and C. Kingsford · 2019
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D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
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Variational inference: A review for statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 2017
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UCI machine learning repository, 2017
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A generalized stochastic variational Bayesian hyperparameter learning framework for sparse spectrum Gaussian process regression
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Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception
R. Ouyang and B. K. H. Low · 2018
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Collective online learning of Gaussian processes in massive multi-agent systems
T. N. Hoang, Q. M. Hoang, B. K. H. Low, and J. P. How · 2019
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“Amnesia” – Towards machine learning models that can forget user data very fast
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Implicit posterior variational inference for deep Gaussian processes
H. Yu, Y. Chen, Z. Dai, K. H. Low, and P. Jaillet · 2019
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Stochastic variational inference for Bayesian sparse Gaussian process regression
H. Yu, T. N. Hoang, B. K. H. Low, and P. Jaillet · 2019
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Eternal sunshine of the spotless net: Selective forgetting in deep neural networks
A. Golatkar, A. Achille, and S. Soatto · 2020
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Scalable variational Bayesian kernel selection for sparse Gaussian process regression
T. Teng, J. Chen, Y. Zhang, and B. K. H. Low · 2020
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