Generating realistic stock market order streams

J Li, X Wang, Y Lin, A Sinha, and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 727-734, Feb 2020. Abstract We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial…

Incentivizing Collaboration in a Competition

A Sinha and MP Wellman Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 556–564, May 2019. Abstract Research and design competitions aim to promote innovation or creative production,…

Deception in Finitely Repeated Security Games

TH Nguyen, Y Wang, A Sinha, and MP Wellman 33rd AAAI Conference on Artificial Intelligence, Jan/Feb 2019. Abstract Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities,…

A Learning and Masking Approach to Secure Learning

L Nguyen, S Wang, and A Sinha Ninth Conference on Decision and Game Theory for Security, October 2018. Abstract Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed…

Stackelberg Security Games: Looking Beyond a Decade of Success

A Sinha, F Fang, B An, C Kiekintveld, and M Tambe 27th International Joint Conference on Artificial Intelligence, July 2018. Abstract The Stackelberg Security Game (SSG) model has been immensely influential in security research since it…

Bounding regret in simulated games

S Jecmen, E Brinkman, and A Sinha 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. ICML-18 Workshop on Exploration in RL, July 2018. Abstract We present a bandit-style problem arising from a specific problem in agent-based…

SoK: Security and Privacy in Machine Learning

N Papernot, P McDaniel, A Sinha, and MP Wellman Third IEEE European Symposium on Security and Privacy, April 2018. Abstract Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics,…