Noise-Aware Differentially Private Regression via Meta-Learning

12 June 2024

Papers citing "Noise-Aware Differentially Private Regression via Meta-Learning"

6 / 6 papers shown

Title
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 36 0 0 13 Mar 2025
Practical Equivariances via Relational Conditional Neural Processes Daolang Huang Manuel Haussmann Ulpu Remes S. T. John Grégoire Clarté K. Luck Samuel Kaski Luigi Acerbi BDL 27 3 0 19 Jun 2023
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 110 258 0 13 Oct 2021
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 121 268 0 25 Sep 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 151 948 0 27 Apr 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 220 9,525 0 09 Mar 2017