Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Authors: Mark Huasong Meng, Guangdong Bai, Sin Gee Teo, Zhe Hou, Yan Xiao, Yun Lin, Jin Song Dong | Published: 2022-06-24 | Updated: 2022-10-11
Formal Verification
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
Authors: Aniket Das, Bernhard Schölkopf, Michael Muehlebach | Published: 2022-06-07 | Updated: 2022-10-10
SoK: Certified Robustness for Deep Neural Networks
Authors: Linyi Li, Tao Xie, Bo Li | Published: 2020-09-09 | Updated: 2023-04-12
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Authors: Pranjal Awasthi, Natalie Frank, Mehryar Mohri | Published: 2020-04-28
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Authors: Florian Tramèr, Dan Boneh | Published: 2018-06-08 | Updated: 2019-02-27