Fetching the paper…
Reading the bibliography…
We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
A visual vocabulary for flower classification
Maria-Elena Nilsback and Andrew Zisserman · 2006
Earlier work this paper cites.
Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
Earlier work this paper cites.
Zero-data learning of new tasks
Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio · 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
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
Earlier work this paper cites.
Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
Earlier work this paper cites.
Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
Earlier work this paper cites.
Simon Lacoste-Julien, Mark Schmidt, and Francis Bach · 2012
Earlier work this paper cites.
Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
Earlier work this paper cites.
Devise: A deep visual-semantic embedding model
Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
Earlier work this paper cites.
Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Fast rates for exp-concave empirical risk minimization
Tomer Koren and Kfir Y Levy · 2015
Earlier work this paper cites.
Predicting deep zero-shot convolutional neural networks using textual descriptions
Jimmy Lei Ba, Kevin Swersky, Sanja Fidler, et al · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman and Lingxiao Wang · 2018
Cited alongside, same era.
Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
Jaewoo Lee and Daniel Kifer · 2018
Cited alongside, same era.
Scalable private learning with PATE
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Later among the works it cites.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Later among the works it cites.
Fast dimension independent private adagrad on publicly estimated subspaces
Peter Kairouz, Mónica Ribero, Keith Rush, and Abhradeep Thakurta · 2020
Later among the works it cites.
Differentially private learning with small public data
Jun Wang and Zhi-Hua Zhou · 2020
Later among the works it cites.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
Later among the works it cites.
Deep leakage from gradients
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson · 2018
Cited alongside, same era.
Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganière, Noman Mohammed, and Yang Wang · 2018
Cited alongside, same era.
Three tools for practical differential privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, and Giorgio Patrini · 2018
Cited alongside, same era.
Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Assessing differentially private deep learning with membership inference
Daniel Bernau, Philip-William Grassal, Jonas Robl, and Florian Kerschbaum · 2019
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.
Ligeng Zhu and Song Han · 2020
Later among the works it cites.
Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
Later among the works it cites.
Lqf: Linear quadratic fine-tuning
Alessandro Achille, Aditya Golatkar, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
Later among the works it cites.
Differentially private learning with adaptive clipping
Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy · 2021
Later among the works it cites.
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, Alina Oprea, and Colin Raffel · 2021
Later among the works it cites.
Gaussian differential privacy
Jinshuo Dong, Aaron Roth, and Weijie Su · 2021
Later among the works it cites.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
Later among the works it cites.
Dp-sgd vs pate: Which has less disparate impact on model accuracy?
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, and Andrew Trask · 2021
Later among the works it cites.
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, et al · 2021
Later among the works it cites.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
Later among the works it cites.
Understanding clipping for federated learning: Convergence and client-level differential privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Jinfeng Yi · 2021
Later among the works it cites.
Bypassing the ambient dimension: Private {sgd} with gradient subspace identification
Yingxue Zhou, Steven Wu, and Arindam Banerjee · 2021
Later among the works it cites.