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Adaptive optimization methods have become the default solvers for many machine learning tasks.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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Learning word vectors for sentiment analysis
Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Rmsprop: Divide the gradient by a running average of its recent magnitude
Hinton, G., Srivastava, N., and Swersky, K · 2012
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, R., Smith, A., and Thakurta, A · 2014
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Heterogeneous differential privacy
Alaggan, M., Gambs, S., and Kermarrec, A.-M · 2015
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Conservative or liberal? personalized differential privacy
Jorgensen, Z., Yu, T., and Cormode, G · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Variants of rmsprop and adagrad with logarithmic regret bounds
Mukkamala, M. C. and Hein, M · 2017
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Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Private stochastic non-convex optimization: Adaptive algorithms and tighter generalization bounds
Zhou, Y., Chen, X., Hong, M., Wu, Z. S., and Banerjee, A · 2020
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Feo2: Federated learning with opt-out differential privacy
Aldaghri, N., Mahdavifar, H., and Beirami, A · 2021
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Public data-assisted mirror descent for private model training
Amid, E., Ganesh, A., Mathews, R., Ramaswamy, S., Song, S., Steinke, T., Suriyakumar, V. M., Thakkar, O., and Thakurta, A · 2021
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Differentially private learning with adaptive clipping
Andrew, G., Thakkar, O., McMahan, H. B., and Ramaswamy, S · 2021
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Private adaptive gradient methods for convex optimization
Asi, H., Duchi, J., Fallah, A., Javidbakht, O., and Talwar, K · 2021
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TensorFlow Federated Stack Overflow dataset, 2019
Authors, T. T. F · 2019
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Rényi differential privacy of the sampled gaussian mechanism
Mironov, I., Talwar, K., and Zhang, L · 2019
Cited alongside, same era.
Adaclip: Adaptive clipping for private sgd
Pichapati, V., Suresh, A. T., Yu, F. X., Reddi, S. J., and Kumar, S · 2019
Cited alongside, same era.
Why are adaptive methods good for attention models?
Zhang, J., Karimireddy, S. P., Veit, A., Kim, S., Reddi, S. J., Kumar, S., and Sra, S · 2020
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(nearly) dimension independent private erm with adagrad rates via publicly estimated subspaces
Kairouz, P., Diaz, M. R., Rush, K., and Thakurta, A
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Practical and private (deep) learning without sampling or shuffling
Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., and Xu, Z
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Adaptive federated optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2021
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Evading the curse of dimensionality in unconstrained private glms via private gradient descent
Song, S., Steinke, T., Thakkar, O., and Thakurta, A · 2021
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A field guide to federated optimization
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., et al · 2021
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Bypassing the ambient dimension: Private sgd with gradient subspace identification
Zhou, Y., Wu, Z. S., and Banerjee, A · 2021
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