Fetching the paper…
Reading the bibliography…
Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points.
Note on a method for calculating corrected sums of squares and products
B. Welford · 1962
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
Earlier work this paper cites.
Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2011
Earlier work this paper cites.
Private convex optimization for empirical risk minimization with applications to high-dimensional regression
D. Kifer, A. D. Smith, and A. Thakurta · 2012
Earlier work this paper cites.
Differential privacy for functions and functional data
R. Hall, A. Rinaldo, and L. A. Wasserman · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. D. Sarwate · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. D. Smith, and A. Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
Learning statistics with privacy, aided by the flip of a coin, 2014
Ú. Erlingsson · 2014
Earlier work this paper cites.
The reusable holdout: Preserving validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Deep roto-translation scattering for object classification
E. Oyallon and S. Mallat · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Nearly optimal private LASSO
K. Talwar, A. Thakurta, and L. Zhang · 2015
Earlier work this paper cites.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Earlier work this paper cites.
Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. D. Smith, T. Steinke, U. Stemmer, and J. R. Ullman · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Earlier work this paper cites.
Learning with privacy at scale, 2017
Apple Differential Privacy Team · 2017
Earlier work this paper cites.
A downsampled variant of imagenet as an alternative to the cifar datasets
P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
Earlier work this paper cites.
Generalization for adaptively-chosen estimators via stable median
V. Feldman and T. Steinke · 2017
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
Earlier work this paper cites.
Understanding the sparse vector technique for differential privacy
M. Lyu, D. Su, and N. Li · 2017
Earlier work this paper cites.
Don’t decay the learning rate, increase the batch size
S. L. Smith, P.-J. Kindermans, C. Ying, and Q. V. Le · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
X. Wu, F. Li, A. Kumar, K. Chaudhuri, S. Jha, and J. F. Naughton · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
Earlier work this paper cites.
The U.S. census bureau adopts differential privacy
J. M. Abowd · 2018
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Cited alongside, same era.
Tensorflow privacy
Google · 2018
Cited alongside, same era.
The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning
S. Ma, R. Bassily, and M. Belkin · 2018
Cited alongside, same era.
An empirical model of large-batch training
S. McCandlish, J. Kaplan, D. Amodei, and O. D. Team · 2018
Cited alongside, same era.
Guaranteed deterministic bounds on the total variation distance between univariate mixtures
F. Nielsen and K. Sun · 2018
Bypassing the ambient dimension: Private SGD with gradient subspace identification
Y. Zhou, S. Wu, and A. Banerjee · 2020
Later among the works it cites.
Large-scale differentially private BERT
R. Anil, B. Ghazi, V. Gupta, R. Kumar, and P. Manurangsi · 2021
Later among the works it cites.
Private stochastic convex optimization: Optimal rates in L1 geometry
H. Asi, V. Feldman, T. Koren, and K. Talwar · 2021
Later among the works it cites.
Label-only membership inference attacks
C. A. Choquette-Choo, F. Tramèr, N. Carlini, and N. Papernot · 2021
Later among the works it cites.
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates
F. Dörmann, O. Frisk, L. N. Andersen, and C. F. Pedersen · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Scalable private learning with PATE
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
Cited alongside, same era.
Measuring the effects of data parallelism on neural network training
C. J. Shallue, J. Lee, J. Antognini, J. Sohl-Dickstein, R. Frostig, and G. E. Dahl · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
Cited alongside, same era.
Distribution density, tails, and outliers in machine learning: Metrics and applications
N. Carlini, U. Erlingsson, and N. Papernot · 2019
Cited alongside, same era.
Augment your batch: better training with larger batches
E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, and D. Soudry · 2019
Cited alongside, same era.
V. Feldman, A. McMillan, and K. Talwar · 2021
Later among the works it cites.
Drawing multiple augmentation samples per image during training efficiently decreases test error
S. Fort, A. Brock, R. Pascanu, S. De, and S. L. Smith · 2021
Later among the works it cites.
Learning and evaluating a differentially private pre-trained language model
S. Hoory, A. Feder, A. Tendler, S. Erell, A. Peled-Cohen, I. Laish, H. Nakhost, U. Stemmer, A. Benjamini, A. Hassidim, et al · 2021
Later among the works it cites.
Tight accounting in the shuffle model of differential privacy
A. Koskela, M. A. Heikkilä, and A. Honkela · 2021
Later among the works it cites.
Large language models can be strong differentially private learners
X. Li, F. Tramèr, P. Liang, and T. Hashimoto · 2021
Later among the works it cites.
Ml-doctor: Holistic risk assessment of inference attacks against machine learning models
Y. Liu, R. Wen, X. He, A. Salem, Z. Zhang, M. Backes, E. D. Cristofaro, M. Fritz, and Y. Zhang · 2021
Later among the works it cites.
Scalable differential privacy with sparse network finetuning
Z. Luo, D. J. Wu, E. Adeli, and L. Fei-Fei · 2021
Later among the works it cites.
Adversary instantiation: Lower bounds for differentially private machine learning
M. Nasr, S. Songi, A. Thakurta, N. Papemoti, and N. Carlin · 2021
Later among the works it cites.
Hyperparameter tuning with renyi differential privacy
N. Papernot and T. Steinke · 2021
Later among the works it cites.
Tempered sigmoid activations for deep learning with differential privacy
N. Papernot, A. Thakurta, S. Song, S. Chien, and Ú. Erlingsson · 2021
Later among the works it cites.
Towards understanding the impact of model size on differential private classification
Y. Shen, Z. Wang, R. Sun, and X. Shen · 2021
Later among the works it cites.
Evading the curse of dimensionality in unconstrained private glms
S. Song, T. Steinke, O. Thakkar, and A. Thakurta · 2021
Later among the works it cites.
Chasing your long tails: Differentially private prediction in health care settings
V. M. Suriyakumar, N. Papernot, A. Goldenberg, and M. Ghassemi · 2021
Later among the works it cites.
Differentially private learning with adaptive clipping
O. Thakkar, G. Andrew, and H. B. McMahan · 2021
Later among the works it cites.
Differentially private learning needs better features (or much more data)
F. Tramèr and D. Boneh · 2021
Later among the works it cites.
Differentially private deep learning under the fairness lens
C. Tran, M. H. Dinh, and F. Fioretto · 2021
Later among the works it cites.
Opacus: User-friendly differential privacy library in PyTorch
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov · 2021
Later among the works it cites.
Scaling vision transformers
X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer · 2021
Later among the works it cites.
Wide network learning with differential privacy
H. Zhang, I. Mironov, and M. Hejazinia · 2021
Later among the works it cites.
Reconstructing training data with informed adversaries
B. Balle, G. Cherubin, and J. Hayes · 2022
Closest in time.
Scalable and efficient training of large convolutional neural networks with differential privacy
Z. Bu, J. Mao, and S. Xu · 2022
Closest in time.
Mixed differential privacy in computer vision
A. Golatkar, A. Achille, Y. Wang, A. Roth, M. Kearns, and S. Soatto · 2022
Closest in time.
Differentially private training of residual networks with scale normalisation
H. Klause, A. Ziller, D. Rueckert, K. Hammernik, and G. Kaissis · 2022
Closest in time.
Toward training at imagenet scale with differential privacy
A. Kurakin, S. Chien, S. Song, R. Geambasu, A. Terzis, and A. Thakurta · 2022
Closest in time.
Federated learning with formal differential privacy guarantees, 2022
B. McMahan and A. Thakurta · 2022
Closest in time.
Large scale transfer learning for differentially private image classification
H. Mehta, A. Thakurta, A. Kurakin, and A. Cutkosky · 2022
Closest in time.
Revisiting weakly supervised pre-training of visual perception models
M. Singh, L. Gustafson, A. Adcock, V. d. F. Reis, B. Gedik, R. P. Kosaraju, D. Mahajan, R. Girshick, P. Dollár, and L. van der Maaten · 2022
Closest in time.
Debugging differential privacy: A case study for privacy auditing
F. Tramer, A. Terzis, T. Steinke, S. Song, M. Jagielski, and N. Carlini · 2022
Closest in time.
Coca: Contrastive captioners are image-text foundation models
J. Yu, Z. Wang, V. Vasudevan, L. Yeung, M. Seyedhosseini, and Y. Wu · 2022
Closest in time.