Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification

29 June 2020

Papers citing "Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification"

9 / 9 papers shown

Title
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing Xiaofeng Lin Qifan Song Yi Tian Xu Belinda Zeng Guang Cheng SSL 13 0 0 26 Jan 2024
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data Ching-Yun Ko Pin-Yu Chen Jeet Mohapatra Payel Das Lucani E. Daniel 19 3 0 06 Oct 2022
Stability Analysis and Generalization Bounds of Adversarial Training Jiancong Xiao Yanbo Fan Ruoyu Sun Jue Wang Zhimin Luo AAML 24 30 0 03 Oct 2022
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction Jiacheng Zhu Jielin Qiu Zhuolin Yang Douglas Weber M. Rosenberg Emerson Liu Bo-wen Li Ding Zhao OOD 28 13 0 02 Aug 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Binghui Li Jikai Jin Han Zhong J. Hopcroft Liwei Wang OOD 68 27 0 27 May 2022
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability Idan Attias Steve Hanneke Yishay Mansour 27 15 0 11 Feb 2022
Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen Yuan Cao Quanquan Gu AAML SILM 26 10 0 31 Dec 2021
When and How Mixup Improves Calibration Linjun Zhang Zhun Deng Kenji Kawaguchi James Y. Zou UQCV 20 67 0 11 Feb 2021
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 60 230 0 25 May 2018