Understanding Reconstruction Attacks with the Neural Tangent Kernel and
Dataset Distillation

Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation

2 February 2023

Ramin Hasani

Mathias Lechner

Alexander Amini

Daniela Rus

Papers citing "Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation"

10 / 10 papers shown

Title
FairDD: Fair Dataset Distillation via Synchronized Matching Qihang Zhou Shenhao Fang Shibo He Wenchao Meng Jiming Chen FedML DD 72 1 0 29 Nov 2024
Slowing Down Forgetting in Continual Learning Pascal Janetzky Tobias Schlagenhauf Stefan Feuerriegel CLL 19 0 0 11 Nov 2024
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation Shaobo Wang Yantai Yang Qilong Wang Kaixin Li Linfeng Zhang Junchi Yan DD 28 4 0 22 Aug 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 37 3 0 25 Feb 2024
How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features Simone Bombari Marco Mondelli AAML 14 4 0 20 May 2023
Efficient Dataset Distillation Using Random Feature Approximation Noel Loo Ramin Hasani Alexander Amini Daniela Rus DD 64 95 0 21 Oct 2022
A Kernel-Based View of Language Model Fine-Tuning Sadhika Malladi Alexander Wettig Dingli Yu Danqi Chen Sanjeev Arora VLM 66 60 0 11 Oct 2022
Dataset Condensation with Differentiable Siamese Augmentation Bo-Lu Zhao Hakan Bilen DD 189 288 0 16 Feb 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 267 1,798 0 14 Dec 2020
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 245 80 0 11 Dec 2020