Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

In recent years, with rapid technological advancement in both computing hardware and
algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

The field of defense strategies against adversarial attacks has significantly grown over the
last years, but progress is hampered as the evaluation of adversarial defenses is often …

The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

Weakly supervised semantic segmentation produces a pixel-level localization from class
labels; but a classifier trained on such labels is likely to restrict its focus to a small …

Adversarial training and its variants have become de facto standards for learning robust
deep neural networks. In this paper, we explore the landscape around adversarial training in …

A recent line of work focused on making adversarial training computationally efficient for
deep learning models. In particular, Wong et al.(2020) showed that $\ell_\infty $-adversarial …

Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are
typically over-parameterized. The over-parameterization may result in undesirably large …

Contrastive learning (CL) can learn generalizable feature representations and achieve state-
of-the-art performance of downstream tasks by finetuning a linear classifier on top of it …