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Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2012
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
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One weird trick for parallelizing convolutional neural networks, 2014
Alex Krizhevsky · 2014
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Efficient mini-batch training for stochastic optimization
Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J. Smola · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015
Sergey Ioffe and Christian Szegedy · 2015
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Adding gradient noise improves learning for very deep networks, 2015
Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, D. Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Nearly optimal private lasso
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima, 2016
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Bolt-on differential privacy for scalable stochastic gradient descent-based analytics, 2016
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey F. Naughton · 2016
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour, 2017
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Earlier work this paper cites.
Decoupled weight decay regularization, 2017
Ilya Loshchilov and Frank Hutter · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
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The effectiveness of data augmentation in image classification using deep learning, 2017
Luis Perez and Jason Wang · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Cited alongside, same era.
Large batch training of convolutional networks, 2017
Yang You, Igor Gitman, and Boris Ginsburg · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data, 2018
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2018
Cited alongside, same era.
Compiling machine learning programs via high-level tracing
Roy Frostig, Matthew Johnson, and Chris Leary · 2018
On the generalization benefit of noise in stochastic gradient descent, 2020
Samuel L. Smith, Erich Elsen, and Soham De · 2020
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Enabling fast differentially private sgd via just-in-time compilation and vectorization
Pranav Subramani, Nicholas Vadivelu, and Gautam Kamath · 2020
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Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2020
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Differentially private learning with adaptive clipping
Galen Andrew, Om Thakkar, Hugh Brendan McMahan, and Swaroop Ramaswamy · 2021
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Private adaptive gradient methods for convex optimization
Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, and Kunal Talwar · 2021
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Cited alongside, same era.
How to start training: The effect of initialization and architecture, 2018
Boris Hanin and David Rolnick · 2018
Cited alongside, same era.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks, 2018
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
On the convergence and calibration of deep learning with differential privacy, 2021
Zhiqi Bu, Hua Wang, and Qi Long · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2021
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Scenic: A JAX library for computer vision research and beyond
Mostafa Dehghani, Alexey Gritsenko, Anurag Arnab, Matthias Minderer, and Yi Tay · 2021
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Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
Friedrich Dormann, Osvald Frisk, Lars Norvang Andersen, and Christian Fischer Pedersen · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Learning and evaluating a differentially private pre-trained language model
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, and Yossi Matias · 2021
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Ml-doctor: Holistic risk assessment of inference attacks against machine learning models, 2021
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang · 2021
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Extreme memorization via scale of initialization
Harsh Mehta, Ashok Cutkosky, and Behnam Neyshabur · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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MLP-mixer: An all-MLP architecture for vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Peter Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
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Differentially private fine-tuning of language models, 2021
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2021
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Scaling vision transformers, 2021
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2021
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Reconstructing training data with informed adversaries, 2022
Borja Balle, Giovanni Cherubin, and Jamie Hayes · 2022
Closest in time.
Unlocking high-accuracy differentially private image classification through scale, 2022
Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, and Borja Balle · 2022
Closest in time.
Toward training at imagenet scale with differential privacy, 2022
Alexey Kurakin, Shuang Song, Steve Chien, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2022
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