Globally-Robust Neural Networks

Globally-Robust Neural Networks

16 February 2021

Matt Fredrikson

Papers citing "Globally-Robust Neural Networks"

15 / 15 papers shown

Title
Graph of Attacks: Improved Black-Box and Interpretable Jailbreaks for LLMs Mohammad Akbar-Tajari Mohammad Taher Pilehvar Mohammad Mahmoody AAML 46 0 0 26 Apr 2025
On the uncertainty principle of neural networks Jun-Jie Zhang Dong-xiao Zhang Jian-Nan Chen L. Pang Deyu Meng 52 2 0 17 Jan 2025
Adversarial Robustification via Text-to-Image Diffusion Models Daewon Choi Jongheon Jeong Huiwon Jang Jinwoo Shin DiffM 27 1 0 26 Jul 2024
SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing Meiyu Zhong Ravi Tandon 21 3 0 03 Jul 2024
Verifiable Boosted Tree Ensembles Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Giulio Ermanno Pibiri AAML 25 0 0 22 Feb 2024
1-Lipschitz Neural Networks are more expressive with N-Activations Bernd Prach Christoph H. Lampert AAML FAtt 6 0 0 10 Nov 2023
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization Mahyar Fazlyab Taha Entesari Aniket Roy Ramalingam Chellappa AAML 11 11 0 29 Sep 2023
When to Trust AI: Advances and Challenges for Certification of Neural Networks M. Kwiatkowska Xiyue Zhang AAML 6 8 0 20 Sep 2023
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion Zhuoqun Huang Neil G. Marchant Keane Lucas Lujo Bauer O. Ohrimenko Benjamin I. P. Rubinstein AAML 13 14 0 31 Jan 2023
Improved techniques for deterministic l2 robustness Sahil Singla S. Feizi AAML 10 9 0 15 Nov 2022
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks Bernd Prach Christoph H. Lampert 22 35 0 05 Aug 2022
Provably Adversarially Robust Nearest Prototype Classifiers Václav Voráček Matthias Hein AAML 13 11 0 14 Jul 2022
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Yujia Huang Huan Zhang Yuanyuan Shi J Zico Kolter Anima Anandkumar 14 76 0 02 Nov 2021
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 105 349 0 04 Oct 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 17 25 0 20 Mar 2021