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Differentially private (DP) optimization is the standard paradigm to learn large neural networks that are accurate and privacy-preserving.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Torchvision the machine-vision package of torch
Marcel, S. and Rodriguez, Y · 2010
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Efficient per-example gradient computations
Goodfellow, I · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 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
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.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Rényi differential privacy
Mironov, I · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Constructing datasets for multi-hop reading comprehension across documents
Welbl, J., Stenetorp, P., and Riedel, S · 2018
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Dong, J., Roth, A., and Su, W. J · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Efficient per-example gradient computations in convolutional neural networks
Rochette, G., Manoel, A., and Tramel, E. W · 2019
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Numerical composition of differential privacy
Gopi, S., Lee, Y. T., and Wutschitz, L · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Large language models can be strong differentially private learners
Li, X., Tramer, F., Liang, P., and Hashimoto, T · 2021
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Compacter: Efficient low-rank hypercomplex adapter layers
Mahabadi, R. K., Henderson, J., and Ruder, S · 2021
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Enabling fast differentially private sgd via just-in-time compilation and vectorization
Subramani, P., Vadivelu, N., and Kamath, G · 2021
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Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Deep learning with gaussian differential privacy
Bu, Z., Dong, J., Long, Q., and Su, W. J · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
Computing tight differential privacy guarantees using fft
Koskela, A., Jälkö, J., and Honkela, A · 2020
Cited alongside, same era.
Scaling up differentially private deep learning with fast per-example gradient clipping
Lee, J. and Kifer, D · 2020
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Tramer, F. and Boneh, D · 2020
Cited alongside, same era.
Opacus: User-friendly differential privacy library in PyTorch
Yousefpour, A., Shilov, I., Sablayrolles, A., Testuggine, D., Prasad, K., Malek, M., Nguyen, J., Ghosh, S., Bharadwaj, A., Zhao, J., Cormode, G., and Mironov, I · 2021
Later among the works it cites.
Differentially private fine-tuning of language models
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al · 2021
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Optimal accounting of differential privacy via characteristic function
Zhu, Y., Dong, J., and Wang, Y.-X · 2021
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Differentially private bias-term only fine-tuning of foundation models
Bu, Z., Wang, Y.-X., Zha, S., and Karypis, G · 2022
Closest in time.
Unlocking high-accuracy differentially private image classification through scale
De, S., Berrada, L., Hayes, J., Smith, S. L., and Balle, B · 2022
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Reconstructing training data from trained neural networks
Haim, N., Vardi, G., Yehudai, G., Shamir, O., and Irani, M · 2022
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Toward training at imagenet scale with differential privacy
Kurakin, A., Chien, S., Song, S., Geambasu, R., Terzis, A., and Thakurta, A · 2022
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Large scale transfer learning for differentially private image classification
Mehta, H., Thakurta, A., Kurakin, A., and Cutkosky, A · 2022
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Zero redundancy distributed learning with differential privacy
Bu, Z., Chiu, J., Liu, R., Wang, Y.-X., Zha, S., and Karypis, G · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Closest in time.