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New Directions in Adversarial Machine Learning: From Theory to Applications

Associate Professor Bo An

Nanyang Technological University

Project Description

Systems supported by machine learning (ML) algorithms have brought significant benefit to our daily life. With the growing deployment of such systems, the security of them has become a major concern in many application domains. This project aims to address the security concern in three main directions:

i) analysing the adversarial ML from the game theoretic view,

ii) expanding the adversarial ML to take into account more complex learning paradigms and

iii) considering adversarial ML on graph-structured data.

This project benefits the ML research by providing frameworks for identifying the vulnerability of ML algorithms and developing defense strategies to make ML more secure. Moreover, this project builds connection between game theory and ML research by modelling the attackers and learners as game-players, which enriches the game theoretic frameworks. In addition, this project will develop novel optimization techniques to compute attack and defense strategies, which also enriches the optimization research.

Research Technical Areas

Game theory and economic paradigms

Machine learning

Adversarial examples

Benefits to the society

The deliverable of our project can be potentially used to improve security of many domains, including commercial recommender systems, self-driving vehicles, financial models and smart traffic control systems.

Team's Principal Investigator

Associate Professor Bo An
Nanyang Technological University

Introduction of the Principal Investigator

Prof Bo An is a President’s Council Chair Associate Professor at NTU. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His research interests include artificial intelligence, multi-agent systems, computational game theory, reinforcement learning, and optimization.

Recent Notable Awards

  • AAAI Senior Member, 2019

  • AI’s 10 to Watch, 2018

  • Winner of the Microsoft Collaborative AI Challenge, 2017

Team

Collaborators

Dr. Milind Tambe, University of Southern California
Research Interests: AI, multi-agent systems, computational and behavioral game theory

Associate Professor Yevgeniy Vorobeychik, Washington University
Research Interests: Research game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design