Theoretical Analysis of Adversarial Learning: A Minimax Approach

Theoretical Analysis of Adversarial Learning: A Minimax Approach

13 November 2018

Papers citing "Theoretical Analysis of Adversarial Learning: A Minimax Approach"

11 / 11 papers shown

Title
Wasserstein Distributionally Robust Nonparametric Regression Changyu Liu Yuling Jiao Junhui Wang Jian Huang OOD 34 0 0 12 May 2025
New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization Soroosh Shafieezadeh-Abadeh Liviu Aolaritei Florian Dorfler Daniel Kuhn 66 20 0 31 Dec 2024
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 40 1 0 06 Oct 2023
Robust Ranking Explanations Chao Chen Chenghua Guo Guixiang Ma Ming Zeng Xi Zhang Sihong Xie FAtt AAML 35 0 0 08 Jul 2023
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it Camilo A. Garcia Trillos Nicolas García Trillos 36 5 0 09 Jan 2023
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs Helene Orsini Hongyan Bao Yujun Zhou Xiangrui Xu Yufei Han Longyang Yi Wei Wang Xin Gao Xiangliang Zhang AAML 44 1 0 13 Dec 2022
Finding Dynamics Preserving Adversarial Winning Tickets Xupeng Shi Pengfei Zheng Adam Ding Yuan Gao Weizhong Zhang AAML 29 1 0 14 Feb 2022
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 36 294 0 18 Oct 2021
Attack Transferability Characterization for Adversarially Robust Multi-label Classification Zhuo Yang Yufei Han Xiangliang Zhang AAML 23 4 0 29 Jun 2021
Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier M. Scetbon Rafael Pinot Jamal Atif Y. Chevaleyre AAML 23 29 0 13 Feb 2021
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 49 8 0 03 Nov 2020