@inproceedings{0a2954e96fa44d6f8bb717f196c99afa,
title = "Position paper: Towards a repeated Bayesian stackelberg game model for robustness against adversarial learning",
abstract = "In this position paper, we propose a game theoretic formulation of the adversarial learning problem called a Repeated Bayesian Stackelberg Game (RBSG) that can be used by a prediction mechanism to make itself robust against adversarial examples.",
author = "Prithviraj Dasgupta and Joseph Collins",
note = "Publisher Copyright: Copyright {\textcopyright} 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 2017 AAAI Fall Symposium ; Conference date: 09-11-2017 Through 11-11-2017",
year = "2017",
language = "English (US)",
series = "AAAI Fall Symposium - Technical Report",
publisher = "AI Access Foundation",
pages = "194--195",
booktitle = "FS-17-01",
address = "United States",
}