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Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability

18 July 2021

Papers citing "Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability"

44 / 44 papers shown

Title
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 121 71 0 14 Jun 2022
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 55 104 0 16 Jun 2021
Revisiting the Calibration of Modern Neural Networks Matthias Minderer Josip Djolonga Rob Romijnders F. Hubis Xiaohua Zhai N. Houlsby Dustin Tran Mario Lucic UQCV 106 367 0 15 Jun 2021
On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu Hua Wang Zongyu Dai Qi Long 81 31 0 15 Jun 2021
Numerical Composition of Differential Privacy Sivakanth Gopi Y. Lee Lukas Wutschitz 79 183 0 05 Jun 2021
Wide Network Learning with Differential Privacy Huanyu Zhang Ilya Mironov Meisam Hejazinia 58 27 0 01 Mar 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 100 42 0 05 Feb 2021
Differentially Private Bayesian Inference for Generalized Linear Models Tejas D. Kulkarni Hibiki Ito A. Koskela Samuel Kaski Antti Honkela 80 31 0 01 Nov 2020
Local Differential Privacy for Bayesian Optimization Xingyu Zhou Jian Tan 75 25 0 13 Oct 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 124 809 0 26 Feb 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 82 40 0 16 Jan 2020
Efficient Per-Example Gradient Computations in Convolutional Neural Networks G. Rochette Andre Manoel Eric W. Tramel 80 19 0 12 Dec 2019
Deep Learning with Gaussian Differential Privacy Zhiqi Bu Jinshuo Dong Qi Long Weijie J. Su FedML 67 209 0 26 Nov 2019
Differentially Private Bayesian Linear Regression G. Bernstein Daniel Sheldon 77 58 0 29 Oct 2019
Rényi Differential Privacy of the Sampled Gaussian Mechanism Ilya Mironov Kunal Talwar Li Zhang 101 287 0 28 Aug 2019
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks Juan Maroñas Roberto Paredes Palacios D. Ramos-Castro UQCV BDL 76 24 0 23 Aug 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 153 482 0 28 May 2019
Differentially Private Markov Chain Monte Carlo Mikko A. Heikkilä Hibiki Ito O. Dikmen Antti Honkela 62 26 0 29 Jan 2019
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference Kumar Shridhar F. Laumann Marcus Liwicki BDL UQCV 83 176 0 08 Jan 2019
A generic framework for privacy preserving deep learning Wenbo Guo Yunzhe Tao Morten Dahl Sui Huang Masashi Sugiyama Daniel Rueckert Lin Lin FedML 104 437 0 09 Nov 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 90 401 0 31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 87 393 0 04 Jul 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov Nathan Fenner Stefano Ermon BDL UQCV 209 636 0 01 Jul 2018
Locally Private Bayesian Inference for Count Models Aaron Schein Zhiwei Steven Wu Alexandra Schofield Mingyuan Zhou Hanna M. Wallach 127 37 0 22 Mar 2018
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain Yu Wang 61 99 0 07 Mar 2018
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 95 502 0 19 Feb 2018
On Connecting Stochastic Gradient MCMC and Differential Privacy Bai Li Changyou Chen Hao Liu Lawrence Carin 76 38 0 25 Dec 2017
Differentially Private Dropout Beyza Ermis A. Cemgil SyDa 45 4 0 30 Nov 2017
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning Nhathai Phan Xintao Wu Han Hu Dejing Dou 106 187 0 18 Sep 2017
Natasha 2: Faster Non-Convex Optimization Than SGD Zeyuan Allen-Zhu ODL 90 246 0 29 Aug 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,877 0 14 Jun 2017
Differentially Private Bayesian Learning on Distributed Data Mikko A. Heikkilä Eemil Lagerspetz Samuel Kaski Kana Shimizu Sasu Tarkoma Antti Honkela FedML 149 59 0 03 Mar 2017
Variational Bayes In Private Settings (VIPS) Mijung Park James R. Foulds Kamalika Chaudhuri Max Welling 81 42 0 01 Nov 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 220 6,172 0 01 Jul 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 256 3,226 0 15 Jun 2016
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks Chunyuan Li Changyou Chen David Carlson Lawrence Carin ODL BDL 102 327 0 23 Dec 2015
On the Differential Privacy of Bayesian Inference Zuhe Zhang Benjamin I. P. Rubinstein Christos Dimitrakakis 38 43 0 22 Dec 2015
Efficient Per-Example Gradient Computations Ian Goodfellow 271 75 0 07 Oct 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 299 751 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 886 9,364 0 06 Jun 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu Wang S. Fienberg Alex Smola 99 249 0 26 Feb 2015
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 124 1,562 0 22 Sep 2013
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks Matthew D. Zeiler Rob Fergus 201 990 0 16 Jan 2013
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 139 1,474 0 06 Mar 2008