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An open problem in differentially private deep learning is hyperparameter optimization (HPO).
Augment your batch: better training with larger batches, 2019
Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., and Soudry, D · 1901
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations, 2019
Hendrycks, D. and Dietterich, T · 1903
Earlier work this paper cites.
Micro-batch training with batch-channel normalization and weight standardization, 2019
Qiao, S., Wang, H., Liu, C., Shen, W., and Yuille, A · 1903
Earlier work this paper cites.
Gaussian differential privacy, 2019
Dong, J., Roth, A., and Su, W. J · 1905
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing, 2019
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 1910
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 1911
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
Polyak, B. and Juditsky, A. B · 1992
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
Qian, N · 1999
Earlier work this paper cites.
Rethinking the hyperparameters for fine-tuning, 2020
Li, H., Chaudhari, P., Yang, H., Lam, M., Ravichandran, A., Bhotika, R., and Soatto, S · 2002
Earlier work this paper cites.
The enron corpus: A new dataset for email classification research
Klimt, B. and Yang, Y · 2004
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
Earlier work this paper cites.
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.
Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
Earlier work this paper cites.
Wilds: A benchmark of in-the-wild distribution shifts, 2020
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., and Liang, P · 2012
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Song, S., Chaudhuri, K., and Sarwate, A. D · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
A primer on monotone operator methods
Ryu, E. K. and Boyd, S. P · 2015
Earlier work this paper cites.
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.
SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Earlier work this paper cites.
Functional map of the world, 2017
Christie, G., Fendley, N., Wilson, J., and Mukherjee, R · 2017
Earlier work this paper cites.
Emnist: an extension of mnist to handwritten letters, 2017
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour, 2017
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Earlier work this paper cites.
Learning differentially private recurrent language models, 2017
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2017
Earlier work this paper cites.
Pointer sentinel mixture models
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2017
Earlier work this paper cites.
Learning with privacy at scale, 2017
Team, A · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: The camelyon17 challenge
Bándi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., Li, Q., Zanjani, F. G., Zinger, S., Fukuta, K., Komura, D., Ovtcharov, V., Cheng, S., Zeng, S., Thagaard, J., Dahl, A. B., Lin, H., Chen, H., Jacobsson, L., Hedlund, M., Çetin, M., Halıcı, E., Jackson, H., Chen, R., Both, F., Franke, J., Küsters-Vandevelde, H., Vreuls, W., Bult, P., van Ginneken, B., van der Laak, J., and Litjens, G · 2018
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity, 2018
Erlingsson, U., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., and Thakurta, A · 2018
Cited alongside, same era.
Unlocking high-accuracy differentially private image classification through scale, 2022
De, S., Berrada, L., Hayes, J., Smith, S. L., and Balle, B · 2022
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Gaussian differential privacy
Dong, J., Roth, A., Su, W. J., et al · 2022
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Mixed differential privacy in computer vision, 2022
Golatkar, A., Achille, A., Wang, Y.-X., Roth, A., Kearns, M., and Soatto, S · 2022
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Recovering private text in federated learning of language models
Gupta, S., Huang, Y., Zhong, Z., Gao, T., Li, K., and Chen, D · 2022
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What you see is what you get: Principled deep learning via distributional generalization, 2022
Kulynych, B., Yang, Y.-Y., Yu, Y., Błasiok, J., and Nakkiran, P · 2022
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization, 2018
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
Cited alongside, same era.
Private selection from private candidates, 2018
Liu, J. and Talwar, K · 2018
Cited alongside, same era.
Differential privacy in practice: Expose your epsilons!
Dwork, C., Kohli, N., and Mulligan, D · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
Cited alongside, same era.
Poission subsampled rényi differential privacy
Zhu, Y. and Wang, Y.-X · 2019
Cited alongside, same era.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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On the SDEs and scaling rules for adaptive gradient algorithms
Malladi, S., Lyu, K., Panigrahi, A., and Arora, S · 2022
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You only need a good embeddings extractor to fix spurious correlations, 2022
Mehta, R., Albiero, V., Chen, L., Evtimov, I., Glaser, T., Li, Z., and Hassner, T · 2022
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Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022
Peng, Z., Dong, L., Bao, H., Ye, Q., and Wei, F · 2022
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Frameworks and results in distributionally robust optimization
Rahimian, H. and Mehrotra, S · 2022
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Tan without a burn: Scaling laws of dp-sgd, 2022
Sander, T., Stock, P., and Sablayrolles, A · 2022
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Recycling scraps: Improving private learning by leveraging intermediate checkpoints, 2022
Shejwalkar, V., Ganesh, A., Mathews, R., Thakkar, O., and Thakurta, A · 2022
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Just fine-tune twice: Selective differential privacy for large language models
Shi, W., Chen, S., Zhang, C., Jia, R., and Yu, Z · 2022
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Considerations for differentially private learning with large-scale public pretraining, 2022
Tramèr, F., Kamath, G., and Carlini, N · 2022
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Unlocking accuracy and fairness in differentially private image classification, 2023
Berrada, L., De, S., Shen, J. H., Hayes, J., Stanforth, R., Stutz, D., Kohli, P., Smith, S. L., and Balle, B · 2023
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Differentially private diffusion models generate useful synthetic images, 2023
Ghalebikesabi, S., Berrada, L., Gowal, S., Ktena, I., Stanforth, R., Hayes, J., De, S., Smith, S. L., Wiles, O., and Balle, B · 2023
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Limits of algorithmic stability for distributional generalization, 2023
Hulkund, N., Suriyakumar, V. M., Killian, T. W., and Ghassemi, M · 2023
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Practical differentially private hyperparameter tuning with subsampling, 2023
Koskela, A. and Kulkarni, T · 2023
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On noisy evaluation in federated hyperparameter tuning, 2023
Kuo, K., Thaker, P., Khodak, M., Nguyen, J., Jiang, D., Talwalkar, A., and Smith, V · 2023
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Differentially private adaptive optimization with delayed preconditioners, 2023
Li, T., Zaheer, M., Liu, K. Z., Reddi, S. J., McMahan, H. B., and Smith, V · 2023
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Effectively using public data in privacy preserving machine learning
Nasr, M., Mahloujifar, S., Tang, X., Mittal, P., and Houmansadr, A · 2023
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Differentially private image classification by learning priors from random processes
Tang, X., Panda, A., Sehwag, V., and Mittal, P · 2023
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DP-hyPO: An adaptive private framework for hyperparameter optimization
Wang, H., Gao, S., Zhang, H., Su, W. J., and Shen, M · 2023
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Fine-tuning language models with just forward passes, 2024
Malladi, S., Gao, T., Nichani, E., Damian, A., Lee, J. D., Chen, D., and Arora, S · 2024
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The importance of feature preprocessing for differentially private linear optimization
Sun, Z., Suresh, A. T., and Menon, A. K · 2024
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Private fine-tuning of large language models with zeroth-order optimization, 2024
Tang, X., Panda, A., Nasr, M., Mahloujifar, S., and Mittal, P · 2024
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