Call for Papers
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 adversary” is crucial for expanding the learning capability, ensuring trustworthy decision making, and enhancing generalizability of machine learning and data mining methods.
This workshop aims to bring together researchers and practitioners to foster and define the foundations of the research in adversarial learning methods for machine learning (ML) and data mining (DM), especially for emerging fields and applications beginning to adopting ML+DM and requiring multidiscipline knowledge and cross-domain expertise, such as health care, bioinformatics, finance, and autonomous vehicles, among others. This workshop also aims to bridge theory and practice by encouraging theoretical studies motivated by the adversarial ML/DM problems, such as robust (minimax) optimization and game theory. The program of this workshop will include: (i) invited talks covering different aspects and recent advances of adversarial learning methods, and (ii) open call track for paper submissions. Submitted papers will be peer-reviewed by the technical program committee. Accepted papers will be presented in the format of oral (lightning talks) and poster. Each accepted paper will be maintained on this website.
Topics of interest include but are not limited to:
Adversarial attacks (e.g. evasion, poison and model inversion) and defenses in machine learning and data mining
Robustness certification and property verification techniques
Representation learning, knowledge discovery and model generalizability
Model robustness against model compression (e.g. network pruning and quantization)
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
Trustworthy data mining and machine learning
Novel applications and innovations using adversarial machine learning and data mining