A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

27 February 2022

Trung Le

Papers citing "A Unified Wasserstein Distributional Robustness Framework for Adversarial Training"

27 / 27 papers shown

Title
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization Shuang Liu Yihan Wang Yifan Zhu Yibo Miao Xiao-Shan Gao 61 0 0 06 Mar 2025
Wasserstein distributional adversarial training for deep neural networks Xingjian Bai Guangyi He Yifan Jiang Jan Obloj OOD 49 0 0 13 Feb 2025
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training Junhao Dong Xinghua Qu Zhiyuan Wang Yew-Soon Ong AAML 48 1 0 05 Nov 2024
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation Anh-Vu Bui L. Vuong Khanh Doan Trung Le Paul Montague Tamas Abraham Dinh Q. Phung KELM DiffM 26 8 0 21 Oct 2024
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls Aras Selvi Eleonora Kreacic Mohsen Ghassemi Vamsi K. Potluru T. Balch Manuela Veloso 29 0 0 18 Jul 2024
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models Xiaohao Yang He Zhao Dinh Q. Phung Wray L. Buntine Lan Du ALM ELM 66 2 0 13 Jun 2024
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning Minh-Tuan Tran Trung Le Xuan-May Le Mehrtash Harandi Dinh Q. Phung CLL 27 6 0 21 Mar 2024
Towards Fairness-Aware Adversarial Learning Yanghao Zhang Tianle Zhang Ronghui Mu Xiaowei Huang Wenjie Ruan 24 4 0 27 Feb 2024
Optimal Transport for Structure Learning Under Missing Data Vy Vo He Zhao Trung Le Edwin V. Bonilla Dinh Q. Phung CML 38 3 0 23 Feb 2024
On robust overfitting: adversarial training induced distribution matters Runzhi Tian Yongyi Mao OOD 28 1 0 28 Nov 2023
Upper and lower bounds for the Lipschitz constant of random neural networks Paul Geuchen Thomas Heindl Dominik Stöger Felix Voigtlaender AAML 27 0 0 02 Nov 2023
Flow-based Distributionally Robust Optimization Chen Xu Jonghyeok Lee Xiuyuan Cheng Yao Xie OOD 26 4 0 30 Oct 2023
Wasserstein distributional robustness of neural networks Xingjian Bai Guangyi He Yifan Jiang J. Obłój OOD AAML 8 6 0 16 Jun 2023
Optimal Transport Model Distributional Robustness Van-Anh Nguyen Trung Le Anh Tuan Bui Thanh-Toan Do Dinh Q. Phung OOD 30 3 0 07 Jun 2023
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport Vy Vo Trung Le L. Vuong He Zhao Edwin V. Bonilla Dinh Q. Phung OT 21 4 0 25 May 2023
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization Anh-Vu Bui Trung Le He Zhao Quan Hung Tran Paul Montague Dinh Q. Phung AAML 32 0 0 26 Apr 2023
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense B. Le Shahroz Tariq Simon S. Woo AAML 11 0 0 21 Mar 2023
Certified Robust Neural Networks: Generalization and Corruption Resistance Amine Bennouna Ryan Lucas Bart P. G. Van Parys 22 10 0 03 Mar 2023
Transformed Distribution Matching for Missing Value Imputation He Zhao Ke Sun Amir Dezfouli Edwin V. Bonilla 19 19 0 20 Feb 2023
Vector Quantized Wasserstein Auto-Encoder Tung-Long Vuong Trung Le He Zhao Chuanxia Zheng Mehrtash Harandi Jianfei Cai Dinh Q. Phung DRL 35 17 0 12 Feb 2023
Efficient and Effective Augmentation Strategy for Adversarial Training Sravanti Addepalli Samyak Jain R. Venkatesh Babu AAML 60 58 0 27 Oct 2022
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport D. Guo Longlong Tian He Zhao Mingyuan Zhou H. Zha OODD 15 18 0 09 Oct 2022
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification D. Guo Zhuo Li Meixi Zheng He Zhao Mingyuan Zhou H. Zha 25 24 0 05 Aug 2022
Adversarial Examples in Random Neural Networks with General Activations Andrea Montanari Yuchen Wu GAN AAML 71 13 0 31 Mar 2022
Global-Local Regularization Via Distributional Robustness Hoang Phan Trung Le Trung-Nghia Phung Tu Bui Nhat Ho Dinh Q. Phung OOD 9 12 0 01 Mar 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 217 675 0 19 Oct 2020
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks He Zhao Thanh-Tuan Nguyen Trung Le Paul Montague O. Vel Tamas Abraham Dinh Q. Phung AAML 8 2 0 13 Oct 2020