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Privacy-preserving machine learning aims to train models on private data without leaking sensitive information.
Note on a method for calculating corrected sums of squares and products
BP Welford · 1962
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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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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Broken promises of privacy: Responding to the surprising failure of anonymization
Paul Ohm · 2009
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Differential privacy for functions and functional data
Rob Hall, Alessandro Rinaldo, and Larry A. Wasserman · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam D. Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Learning statistics with privacy, aided by the flip of a coin
Úlfar Erlingsson · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton · 2015
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Deep roto-translation scattering for object classification
Edouard Oyallon and Stéphane Mallat · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 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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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Learning with privacy at scale
Differential Privacy Team · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang, JAX: composable transformations of Python+NumPy programs, GitHub, 2018; http://github.com/google/jax
2018
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Guaranteed deterministic bounds on the total variation distance between univariate mixtures
Frank Nielsen and Ke Sun · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Places: A 10 million image database for scene recognition
Bolei Zhou, Àgata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Earlier work this paper cites.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Earlier work this paper cites.
Distribution density, tails, and outliers in machine learning: Metrics and applications
Nicholas Carlini, Ulfar Erlingsson, and Nicolas Papernot · 2019
Earlier work this paper cites.
On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Augment your batch: Better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2019
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CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie S. Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
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A. Johnson, T. Pollard, R. Mark, S. Berkowitz, and S. Horng, Mimic-cxr database, version 2.0.0, PhysioNet, 2019; https://doi.org/10.13026/C2JT1Q
2019
Earlier work this paper cites.
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Alistair E W Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-Ying Deng, Roger G Mark, and Steven Horng · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
Earlier work this paper cites.
How does learning rate decay help modern neural networks?
Kaichao You, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2019
Earlier work this paper cites.
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Claudio Fantacci, Jonathan Godwin, Chris Jones, Tom Hennigan, Matteo Hessel, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Lena Martens, Vladimir Mikulik, Tamara Norman, John Quan, George Papamakarios, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Wojciech Stokowiec, and Fabio Viola, The DeepMind JAX Ecosystem, GitHub, 2020; http://github.com/deepmind
2020
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Robustness may be at odds with fairness: An empirical study on class-wise accuracy
Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Disclosure avoidance for the 2020 census: An introduction
U.S. Census Bureau · 2020
Earlier work this paper cites.
Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, and Andrew Trask · 2020
Cited alongside, same era.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
Cited alongside, same era.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Cited alongside, same era.
Inverting gradients - how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 2020
Cited alongside, same era.
Differentially private learning does not bound membership inference
Thomas Humphries, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg, Urs Hengartner, and Florian Kerschbaum · 2020
Cited alongside, same era.
Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2021
Later among the works it cites.
Large-scale robust deep AUC maximization: A new surrogate loss and empirical studies on medical image classification
Zhuoning Yuan, Yan Yan, Milan Sonka, and Tianbao Yang · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Medical imaging deep learning with differential privacy
Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus Makowski, Daniel Rueckert, and Georgios Kaissis · 2021
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Borja Balle, Leonard Berrada, Soham De, Jamie Hayes, Samuel L Smith, and Robert Stanforth, JAX-Privacy: Algorithms for privacy-preserving machine learning in jax, 0.1.0, 2022; http://github.com/deepmind/jax_privacy
2022
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan R. Ullman, and Alina Oprea · 2020
Cited alongside, same era.
A. Johnson, L. Bulgarelli, T. Pollard, S. Horng, L. A. Celi, and R. Mark, Mimic-iv, version 0.4, PhysioNet, 2020; https://doi.org/10.13026/a3wn-hq05
2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Agostina J Larrazabal, Nicolás Nieto, Victoria Peterson, Diego H Milone, and Enzo Ferrante · 2020
Cited alongside, same era.
Fair learning with private demographic data
Hussein Mozannar, Mesrob I. Ohannessian, and Nathan Srebro · 2020
Cited alongside, same era.
Fair inputs and fair outputs: The incompatibility of fairness in privacy and accuracy
Bashir Rastegarpanah, Mark Crovella, and Krishna P Gummadi · 2020
Cited alongside, same era.
How differential privacy will affect our understanding of health disparities in the united states
Alexis R Santos-Lozada, Jeffrey T Howard, and Ashton M Verdery · 2020
Cited alongside, same era.
Reconstructing training data with informed adversaries
Borja Balle, Giovanni Cherubin, and Jamie Hayes · 2022
Later among the works it cites.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr · 2022
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2022
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Connect the dots: Tighter discrete approximations of privacy loss distributions
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi · 2022
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Analyzing privacy leakage in machine learning via multiple hypothesis testing: A lesson from fano
Chuan Guo, Alexandre Sablayrolles, and Maziar Sanjabi · 2022
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Exploring the limits of differentially private deep learning with group-wise clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian · 2022
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Anatomy-XNet: An anatomy aware convolutional neural network for thoracic disease classification in chest x-rays
Uday Kamal, Mohammad Zunaed, Nusrat Binta Nizam, and Taufiq Hasan · 2022
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Deconstructing distributions: A pointwise framework of learning
Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, and Preetum Nakkiran · 2022
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Differentially private training of residual networks with scale normalisation
Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, and Georgios Kaissis · 2022
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Disparate vulnerability to membership inference attacks
Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, and Carmela Troncoso · 2022
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Toward training at imagenet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin-Tat Lee, and Abhradeep Guha Thakurta · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
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Federated learning with formal differential privacy guarantees
Brendan McMahan and Abhradeep Thakurta · 2022
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Differentially private image classification from features
Harsh Mehta, Walid Krichene, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky · 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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Tan without a burn: Scaling laws of DP-SGD
Tom Sander, Pierre Stock, and Alexandre Sablayrolles · 2022
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How unfair is private learning?
Amartya Sanyal, Yaxi Hu, and Fanny Yang · 2022
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Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 2022
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Debugging differential privacy: A case study for privacy auditing
Florian Tramer, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, and Nicholas Carlini · 2022
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Differentially private fine-tuning of language models
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 · 2022
Later among the works it cites.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Improving the fairness of chest x-ray classifiers
Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Pfohl, and Marzyeh Ghassemi · 2022
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Soroosh Tayebi Arasteh, Mahshad Lotfinia, Teresa Nolte, Marwin Saehn, Peter Isfort, Christiane Kuhl, Sven Nebelung, Georgios Kaissis, and Daniel Truhn · 2023
Closest in time.
Private, fair and accurate: Training large-scale, privacy-preserving ai models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, and Georgios Kaissis · 2023
Closest in time.
Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
Closest in time.
Fine-tuning with differential privacy necessitates an additional hyperparameter search
Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta · 2023
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Symbolic discovery of optimization algorithms
Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, and Quoc V. Le · 2023
Closest in time.
Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, and Neil Houlsby · 2023
Closest in time.
Why is public pretraining necessary for private model training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2023
Closest in time.
Google, Differential privacy accounting library, GitHub, 2023; https://github.com/google/differential-privacy
2023
Closest in time.
Bounding training data reconstruction in DP-SGD
Jamie Hayes, Saeed Mahloujifar, and Borja Balle · 2023
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Equivariant differentially private deep learning
Florian A. Hölzl, Daniel Rueckert, and Georgios Kaissis · 2023
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The misuse of auc: What high impact risk assessment gets wrong
Kweku Kwegyir-Aggrey, Marissa Gerchick, Malika Mohan, Aaron Horowitz, and Suresh Venkatasubramanian · 2023
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Pillar: How to make semi-private learning more effective
Francesco Pinto, Yaxi Hu, Fanny Yang, and Amartya Sanyal · 2023
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Differentially private image classification by learning priors from random processes
Xinyu Tang, Ashwinee Panda, Vikash Sehwag, and Prateek Mittal · 2023
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Vip: A differentially private foundation model for computer vision
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, and Chuan Guo · 2023
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