Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning

9 October 2021

Papers citing "Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning"

10 / 10 papers shown

Title
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments Amin Rakhsha Xuezhou Zhang Xiaojin Zhu Adish Singla AAML OffRL 54 37 0 16 Feb 2021
On the Adversarial Robustness of LASSO Based Feature Selection Fuwei Li Lifeng Lai Shuguang Cui AAML 45 19 0 20 Oct 2020
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics Yanchao Sun Da Huo Furong Huang AAML OffRL OnRL 57 49 0 02 Sep 2020
$Q$ -learning with Logarithmic Regret Kunhe Yang Lin F. Yang S. Du 52 59 0 16 Jun 2020
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 90 752 0 31 May 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 94 2,018 0 08 Feb 2019
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation Matthew O'Kelly Aman Sinha Hongseok Namkoong John C. Duchi Russ Tedrake 51 217 0 31 Oct 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 443 3,124 0 04 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 108 2,520 0 26 Oct 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 157 14,831 1 21 Dec 2013