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The success of large neural networks is crucially determined by the availability of data.
The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Conservative or liberal? personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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URL https://ec.europa.eu/commission/sites/beta-political/files/data-protection-factsheet-changes_en.pdf
2018 reform of eu data protection rules · 2018
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Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
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Don’t decay the learning rate, increase the batch size
Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V Le · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2018
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Model-agnostic private learning
Raef Bassily, Om Thakkar, and Abhradeep Guha Thakurta · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Three tools for practical differential privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, and Giorgio Patrini · 2018
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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Differentially private learning with adaptive clipping
Galen Andrew, Om Thakkar, Brendan McMahan, and Swaroop Ramaswamy · 2021
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Combining public and private data
Cecilia Ferrando, Jennifer Gillenwater, and Alex Kulesza · 2021
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2020
Cited alongside, same era.
Fast dimension independent private adagrad on publicly estimated subspaces
Peter Kairouz, Mónica Ribero, Keith Rush, and Abhradeep Thakurta · 2020
Cited alongside, same era.
Understanding gradient clipping in private sgd: A geometric perspective
Xiangyi Chen, Steven Z Wu, and Mingyi Hong · 2020
Cited alongside, same era.
A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima
Zeke Xie, Issei Sato, and Masashi Sugiyama · 2020
Cited alongside, same era.
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, et al · 2020
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2020
Cited alongside, same era.
Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
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Large scale transfer learning for differentially private image classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky · 2022
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Mixed differential privacy in computer vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, and Stefano Soatto · 2022
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Private adaptive optimization with side information
Tian Li, Manzil Zaheer, Sashank Reddi, and Virginia Smith · 2022
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Public data-assisted mirror descent for private model training
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, and Abhradeep Thakurta · 2022
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Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2022
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Understanding clipping for federated learning: Convergence and client-level differential privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, and Jinfeng Yi · 2022
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