Forthcoming Speical Issue

Recent Advances in Adversarial Machine Learning

Aims and Scope

In recent years, adversarial learning methods are shown to be a key technique that leads to exciting breakthroughs and new challenges of many machine learning and data mining tasks. Examples include improved training of generative models (e.g., generative adversarial nets), adversarial robustness of machine learning systems in different domains (e.g., adversarial attacks, defenses, and property verification), and robust representation learning (e.g., adversarial loss for learning embedding), to name a few. Generally speaking, the idea of “learning with an adversary” is crucial for expanding the learning capability, ensuring trustworthy decision making, and enhancing generalizability of machine learning and data mining methods.

Guest Editors

Dr. Pinyu Chen
IBM Thomas J. Watson Research Center, USA
Research Interests:  machine Learning; data Science; cyber Security

Dr. Chojui Hsieth
Department of Computer Science, The University of California, USA
Research Interests: adversarial deep learning; model compression and fast prediction; fast or parallel training; large-scale recommender systems, ranking and active learning

Dr. Bo Li
Department of Computer Science, University of Illinois at Urbana-Champaign, USA
Research Interests: machine learning; machine security; machine privacy; machine game theory

Dr. Sijia Liu

Department of Computer Science and Engineering, Michigan State University, USA

Research Interests: machine learning; deep learning; optimization; computer vision; security; signal processing and date science; developing learning algorithms and theory; robust and explainable artificial intelligence