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
Special Issue Information
The topics of interest include, but are not limited to, the following
▪Adversarial attacks and defenses in machine learning and data mining
▪Provably robust machine learning methods and systems
▪Robustness certification and property verification techniques
▪Representation learning, knowledge discovery and model generalizability
▪Generative models and their applications (e.g., generative adversarial nets)
▪Robust optimization methods and (computational) game theory
▪Explainable and fair machine learning models via adversarial learning techniques
▪Transfer learning, multi-agent adaptation, self-paced learning
▪Privacy and security in machine learning systems
▪Adversarail machine learning for (social) good
▪Novel applications and innovations using adversarial machine learning and data mining
Manuscript Submission Information

Submissions that pass pre-check will be reviewed by at least two reviewers of the specific field. Accepted papers will be published on early access first and sent for copy editing and typesetting. Then all papers will be included in the special issue when it is published.

If you have any queries regarding the special issue or other matters, please feel free to contact the editorial office: