2019 - 2020
1. Adversarial Machine Learning
Exploring the vulnerability of machine learning mdoels to adverarial attacks under both black-box and white-box settings.
Defense machine learning models against adversarial attacks.
Improving adversarial training as a defense technique for model robustness.
Model interpretation and understanding.
Image, video, text, audio adversarial examples.
From Lp-bounded to unrestricted adversarial perturbations.
2. Robust Supervised/Weakly-Supervised Learning
Learning with noisy/adversarial labels.
Learning with input noise.
Learning with adversarial back-door/trojan attacks.
Learning in noisy and dynamic real-world environments.