Learning with Differentially Private (Sliced) Wasserstein Gradients

3 February 2025

David Rodríguez-Vítores

Papers citing "Learning with Differentially Private (Sliced) Wasserstein Gradients"

50 / 60 papers shown

Title
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training Kristjan Greenewald Yuancheng Yu Hao Wang Kai Xu 112 1 0 25 Oct 2024
Privately Learning Smooth Distributions on the Hypercube by Projections Clément Lalanne Sébastien Gadat 50 1 0 16 Sep 2024
Differentially Private Post-Processing for Fair Regression Ruicheng Xian Qiaobo Li Gautam Kamath Han Zhao 52 2 0 07 May 2024
Differentially Private Fair Binary Classifications Hrad Ghoukasian S. Asoodeh FaML 68 2 0 23 Feb 2024
Wasserstein Differential Privacy Chengyi Yang Jiayin Qi Aimin Zhou 26 3 0 23 Jan 2024
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance Ilana Sebag Muni Sreenivas Pydi Jean-Yves Franceschi Alain Rakotomamonjy Mike Gartrell Jamal Atif Alexandre Allauzen 48 3 0 13 Dec 2023
Properties of Discrete Sliced Wasserstein Losses Eloi Tanguy Rémi Flamary J. Delon 37 10 0 19 Jul 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 71 9 0 13 Apr 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FedML FaML 63 8 0 17 Feb 2023
Private Statistical Estimation of Many Quantiles Clément Lalanne Aurélien Garivier Rémi Gribonval 44 6 0 14 Feb 2023
Differential Privacy has Bounded Impact on Fairness in Classification Paul Mangold Michaël Perrot A. Bellet Marc Tommasi 49 19 0 28 Oct 2022
Stochastic Differentially Private and Fair Learning Andrew Lowy Devansh Gupta Meisam Razaviyayn FaML FedML 33 14 0 17 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 104 51 0 02 Oct 2022
Fair learning with Wasserstein barycenters for non-decomposable performance measures Solenne Gaucher Nicolas Schreuder Evgenii Chzhen 55 15 0 01 Sep 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 52 29 0 15 Jun 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 61 26 0 17 May 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 70 227 0 28 Apr 2022
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Ferdinando Fioretto Cuong Tran Pascal Van Hentenryck Keyu Zhu FaML 46 64 0 16 Feb 2022
Private Quantiles Estimation in the Presence of Atoms Clément Lalanne C. Gastaud Nicolas Grislain Aurélien Garivier Rémi Gribonval 29 8 0 15 Feb 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 267 363 0 25 Sep 2021
Transport-based Counterfactual Models Lucas de Lara Alberto González Sanz Nicholas M. Asher Laurent Risser Jean-Michel Loubes 35 31 0 30 Aug 2021
Differentially Private Sliced Wasserstein Distance A. Rakotomamonjy L. Ralaivola 42 24 0 05 Jul 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 56 49 0 24 Jun 2021
Differentially Empirical Risk Minimization under the Fairness Lens Cuong Tran My H. Dinh Ferdinando Fioretto 33 46 0 04 Jun 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 279 494 0 31 Dec 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 97 43 0 19 Oct 2020
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy Tom Farrand Fatemehsadat Mireshghallah Sahib Singh Andrew Trask FedML 37 92 0 10 Sep 2020
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping Jaewoo Lee Daniel Kifer 68 55 0 07 Sep 2020
Fair Regression with Wasserstein Barycenters Evgenii Chzhen Christophe Denis Mohamed Hebiri L. Oneto Massimiliano Pontil 56 107 0 12 Jun 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 53 116 0 11 Jun 2020
Differentially Private Assouad, Fano, and Le Cam Jayadev Acharya Ziteng Sun Huanyu Zhang FedML 52 58 0 14 Apr 2020
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set Philippe C. Besse E. del Barrio Paula Gordaliza Jean-Michel Loubes Laurent Risser FaML 36 65 0 31 Mar 2020
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation Frederik Harder Kamil Adamczewski Mijung Park SyDa 99 102 0 26 Feb 2020
Private Mean Estimation of Heavy-Tailed Distributions Gautam Kamath Vikrant Singhal Jonathan R. Ullman 75 100 0 21 Feb 2020
Differentially Private and Fair Classification via Calibrated Functional Mechanism Jiahao Ding Xinyue Zhang Xiaohuan Li Junyi Wang Rong Yu Miao Pan FaML 43 39 0 14 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 366 42,299 0 03 Dec 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization Laurent Risser Alberto González Sanz Quentin Vincenot Jean-Michel Loubes 47 21 0 15 Aug 2019
Wasserstein Fair Classification Ray Jiang Aldo Pacchiano T. Stepleton Heinrich Jiang Silvia Chiappa 62 177 0 28 Jul 2019
Private Hypothesis Selection Mark Bun Gautam Kamath Thomas Steinke Zhiwei Steven Wu 41 91 0 30 May 2019
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 137 480 0 28 May 2019
Sliced Wasserstein Generative Models Jiqing Wu Zhiwu Huang Dinesh Acharya Wen Li Janine Thoma D. Paudel Luc Van Gool DiffM 113 126 0 10 Apr 2019
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation Chen-Yu Lee Tanmay Batra M. H. Baig Daniel Ulbricht 124 541 0 10 Mar 2019
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy T. Tony Cai Yichen Wang Linjun Zhang 63 168 0 12 Feb 2019
Differentially Private Fair Learning Matthew Jagielski Michael Kearns Jieming Mao Alina Oprea Aaron Roth Saeed Sharifi-Malvajerdi Jonathan R. Ullman FaML FedML 98 151 0 06 Dec 2018
Local Obfuscation Mechanisms for Hiding Probability Distributions Yusuke Kawamoto Takao Murakami 68 26 0 03 Dec 2018
Obtaining fairness using optimal transport theory E. del Barrio Fabrice Gamboa Paula Gordaliza Jean-Michel Loubes FaML 140 181 0 08 Jun 2018
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 83 151 0 01 May 2018
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model Soheil Kolouri Phillip E. Pope Charles E. Martin Gustavo K. Rohde 36 94 0 05 Apr 2018
Generative Modeling using the Sliced Wasserstein Distance Ishani Deshpande Ziyu Zhang Alex Schwing GAN 52 226 0 29 Mar 2018
Collecting Telemetry Data Privately Bolin Ding Janardhan Kulkarni Sergey Yekhanin 48 683 0 05 Dec 2017